{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization\n",
    "\n",
    "Welcome to the first assignment of \"Improving Deep Neural Networks\". \n",
    "\n",
    "Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.  \n",
    "\n",
    "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead to different results. \n",
    "\n",
    "A well chosen initialization can:\n",
    "- Speed up the convergence of gradient descent\n",
    "- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
    "\n",
    "To get started, run the following cell to load the packages and the planar dataset you will try to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jGDeDC5qKjDx2vzyH9IVbMIcE0OHuqZiD/HCVVHiNVV0y/k3Pjo5ZUWElRxLzCQqx0apt49PO3tM0jU/f2cCOLWk4jvVpTDGJiEIDyJQJ2r+aS6Z2ZNyUjog6rcfqcp89OzJYMj+OpMQCnA4ZQYCuPaOQFZWE/TkosobJJPL959uY9dhwuvf27IgnuxTWrEhk3arDaKrG4BGtGHFJWyzWEz8bkU1DePy5sXz76VaOHC5AlAR69WvGTXf1x2TyjHqUJmey4Y43UKslsOx742eaDOrMqHnPEvt/n5Pw+ULkCgchHaLp/9Y9lKflIAg6ERRNI2fTfu/jBgYYbkmDC5jyo7n83mMGzpJytGOFxKYAGwEtwilLzvJwUQkmiYZdW3Jp7Cf1ugZV1fjus62sWZGI2SyhqhohDWw88sxowiOD6zzf4YRcXv3PiirDpofFKjFgaEtunzXwtNasaRqfvbeRrRtScDk9kz0EQT8T32KVeO+rK6uyGxVF5ZX/LOfI4fyqhBGLRSIqOoSnXhmPWadps8ulIIoCko+OJssf/5o936/BvyAPm93z5SR6ykDG/P5C1fo1Ra1SFE/5fQPrbnoFV6n3C01gTARXJX1/ir/I+UeVFdIWbCIvNoGA6DBaXTvyrJR3/BMwsiUNzguqolB+NLfGXoGKw0nC5wtZPOohlo57jKQfV+k2Lt7z0vc4i08YNnAX+pYmZxI5uieS1ewupPa30rBLS8b++VK9P8+KhfGsW3UY2aVSWeHCYZfJzS7j1adX+CxWrold247i1GnHdTJOh8KmNcleCRm15eD+HLZvTPUybOC7xEwQBGK3nOiLuWNLGilJBR6ZkE6nQmZ6CVvWH9Gdw2yWdA1bcVElsx9ZyI8JInHdh7Bl9DTieg1DPWk3Zs8p9FiLeJKcT7MJ/XQVEEz+VjrdP03/gS4g7HnF/NblNtbe/Ap7XvqebQ9/zM8tppNr9LQ8qxhuSYN649DXS9j22KfI5XY0VaX5lIEM/uxhjzdUxeFk0fAHKdydhOJwu6cyVu7gwAfzmbjuXQ93X9qfW/Qb9ioqEcO6M+ijB9wxtmZhNOra6qw80+Lf93ulumsalJc6OBiXTceuEXWaz2yREEUBRTlFQ2WTSNKhPBqHBdR5zVvWJeM4hQGtjqq6jfdxqsfRjuNwyGxZn8KgYS1xOGRsfuZTumjffekvUpIKUTUBzO6dYX5Ec5I69qTN/lgkm4Wm4/v5vF6ymLlk6WssG/84qlN27+xkhZgrh9PpX5fX6vmK4lM5+MkCylJyiBzZnTY3j8MSXPPftiIzn4OfLaQo7giNe7ej3e0TdHuNnorN971PWXJWlUzT8ebdKy97imvSf65zeYRB7TCMm8FpUbT/CFnr9mJrHEyzyQM5ungrm2a95ym6uWATFZOeYNL696qOHf5uBQW7Ej2bCasaORvjiH3ic/q8fGfVYVOQn+69RZMJS7A/AU3DCGgaVv8PdxKlJfoNjTUBCgu83WSnot/gFvwxdy/KqSR2NI3gEFud5wd8Nwmu6RIEOnU7YagtVpNPF2ZOZgkzr/8J2aUQGGTlyht6MmyMfsZi1tESjhwu8NrlqiYTGTEdaJOwC0tIAB1nXVbj+kJ7t+PajLkcXR6LI6+YJoO7ENy6dhp8yT//xbpbX0N1yWiywtFl29n76o9M2fpf/KP0Y7Q5m+JYOu5xVJeM6nCR9udm9r7yA5PWv0tIxxbkbNpP2p+bMPlZaXnNCELaeSfPgLvJQMq8dbr6g3K5ndwtB86oX6qBbwzjZlAnVFlhzfUvkvbnZncTYElCEAUsDQO9Nc4cLgp2HSZ/56EqxePDc1bqdskHiHtnHr1euK2qxqrj3VPZ9vinXvNqmkaLK86Ndlh0iwYkHcr3Oq4qGjGta87KTE7MZ/f2dExmib6DmhMeGUxEVDDTruvOr3N2o8iqvmtTAP9AC207NjmtNQ8YEsOGVUk1xvVONlwWq0SfAc1pGt2g6vzQ0a3ZvC7Za9cqigJ5OeXIsrtlWHGRnW8/24ooCgwZ1drrPhlHi1F0lLsBVFGi1c3j6PvszbXaEYlmU53V1+UKu1sw9qT4rFJhp9LpYusjHzNizlNe12iaxl/TX/DoHqNUOlDsTtbe/CpBraNIX7gZucKOIEnseXkOvZ6/lS4PX+09l6r5bi4gCDWqQRicGYZxM6gTce/OI23h5qofi+NfW2exj/iQJFC0P6XKuOm6GY+hKSqliRmEtHe/Bbe/awoZK3dwdNl2VKeMKzCQvNCmtLt9Ag7Jwmnua+rE1Tf14q3nV+E8KX5ltkh06R5JVDP9H2RN0/ji/U1s2XAEl1NBFEXm/7SHy6/tzqRpnZl4WWd69G7GxjVJVFa6SE0uIDmxAFEUEATw8zPz6Owxp50t2a5TE/oPacGW9SkeBk4QBRqHBnDzzH5s25jKvl0Z+AdYGDupPcPGeHbGb9exCWMmtmfFwoPIsoKmgcksIstalWE7jtOh8Mt3Oxk8spWXi7KkqAadNlFk2KcP1UvfSF9krtqpqwagyQqpv2/QvaY4PhVHfon3CU2jYFciRQdSql64NFlBkRV2PP0V0ZMHVn12jyOaJML6dXALx1ZDlRXC+nc8jacyqA2GcTNA0zQK9yShOFw07tmmqkuHHvEfzPfaSdWIqhHc5kSKeeubLiF73V7doYIoYA4+IXgpmiRG//ocudvi+ePb7WxK15BMEsk7ilh8529ce0svxkzqUPu1nAYdu0bwwJMjmfPldtJTirD5mRk1vi1XXNfD5zWxm9PYujGlatejKCqKAnO/3UF5uYPLr+1OVHQIV97Qs+qazKPFJB3Kp0FDPzp2CUf0paFWCwRB4LZZA+k/NIYNq5NwOhVi2jSiW88omrdshCAIdOvV9JTzXHNzbwYOb8X2jSmoqkbDxv7M/XanR2zuOEWFdlwutaqO7jgBgVYkST/GaDaLZ9Wwgfuz7fOcj4QgX8ePn9P7/KsuhcNzVtLr2Vu8zg344D4Wj3wQpdKJdmwXa/K30ufVGZgD9V3vBmeOYdz+4eRujWf1VbNxFJYhCAKCJDLo04doeeVw3fE+d2h6mESCWke59cqO0e7W8cT+3+c48oo9xwoCoX3a6xZgFwc3ZnOWiKIqKCftoH763w7admxCi1b1W/Ssqhp7d2YQvzeLoBAbA4e35MV3p6BpWq1+jFcvTdBNxtA0WDx/P/H7snnixXEedWCRTUOIbFr3ZAVfCIJAlx5RdOlRu7iUL5rHNKR5jFvxICujhB+/itUdZ/MzYTZ7G+QuPSJ1E2gEAQaNODtJQCcTObKnrrdAkESaTzlRaqGpKrlb45ErHIT2bYc52N9D5+44ks2s20VFU1Tkcv1damjvdkzd/jF7XvmBnI1xBLaMoOuj1xI1qqfueIP6wTBu/0DkCjs5m/aj2J2sue5Fr/qhdbe8SnDrqCpX4smED+3qjrfVoj4ypF0045a95mEQBFFk6o5P+LPf3djzS9AUFclmwRYawvAfniIxPpfFv+8nL6eM9p3DGTelI6uWJODSEcx0ORXWLD/ETXfVPg6Tm13GHz/vYd+uTPwDLYyd3IFho9tUuQAdDplXnlrG0bRiHHYZs1nktx92c9dDQ+gzoLnPeQ/szWLOl9tJSynypTkKuGN1aUcKid2cSv8hMbVe94VARFQwLVo3Iikh3yOOZrFKjJvSUdfw+/lbuG3WQL78cDOKoqIqGlabiaBgK1fdePZ/3M2Bfgz8+EE23vU2qtPl/rz5WTEH+dH3zbsBt87cysufxlVeiSAIaLJC2zsnceizRSguF5pLQbSYkawm2twynoTPF3oZOJO/tUonT4+QdtEM/fKxs/qsBp4YRdz/MA5+sYitD3yIIIkodheq09vFhCjQ6tpRDP/uCa9ThXFH+HPgLPdbbQ2fHVOgH6PmzabpWP1aS1VROLp4K0XxaYS0a0azif1ZuyqJ77/Y5o5vae50eItFIrpFAxIO5OrO0zg0gDc/u7xWO6qcrFKeeXgh9kq5KpHDYpXoO6gFM+4fDMD7r/zF9s1pXtdWL3I+mfh92bz53EqPuNypiGgaxMvvTT0j92N94nIpZKQVExhkrbH8oKzEwXuv/EVSYj4mk4jLpTB0VGtumtGvxmfJOlrC6mUJFOZX0rl7BAOGtcRqPXfv1gV7k4j/7x+UpWQTOaoH7W6fiLVhEI6iMua2mO71gif5Wxn8yUPkbo2naH8Kob3b0XHWZVhCAvijz0zK03Kr2oSZAmxEjurJ6PnP19nN6iqrJGnOSvK2HyS4XTPa3jwOW1iDU1/4D8bQczPwInvDPpaNe6xWGVqN+7Rn6tb/6p4rjDvCjqe+JGvtHjTV7Y7RqiUZWENDuDZjrkcxbk047C5m3TzXWz5FgIjIIPJyy5Fd3ll3JpPIA0+OpGvPU7vfPn5rPZvXJXvZZLNF4rk3J/HrD7vYtlFfb8zmZ+KWmQMYOLyl17lnH12km1FZE6IoMOGyTlx9U686XXc2WLHoIHO/3QGAomhExzRk1qPDajRyOVmlFORXENUs5PRLFs4BxUWVSKJIYLB+78z4jxew7ZGPdL8TTSf045KFL3sdd5aUE//fP0j6aTUmPyvtZ0yi9Y1j66zoXZaSzZ8D7sVVVolcbkfysyBIEpcseZXwQUZ5gC+MxskGXux782dknXhBdQSzRFi/9j7PN+wcw+jfngPcCtMrJj9B7pYDqLKCaDEhShKXLHrZy7CVFNtZvjCePbFHCQ6xMXZyh6rEhoQDuce6W1QzbhpkZ5bqtnsCkGWVDauTamXc9u3K8LnZXLvqsE/DBu5EAl+dRdKOFOke95VIAe643vKF8Vx2bXevJIxzyY4tafz0TazHS8WRxHxefmoZr310mc+MzSYRQTSJCDpXy6wzifG5fP7+RnKzy9CAmFaNmHH/YCKaerZMK0vJ9vmyp6c8AWAJDqDbv6fT7d/Tz2iN6+98A3tecVWSyXFX519XP8vVqT8axd1niGHc/kGUJWfVKlYmWS10eeiqWs1pslkYt/x18rYdJHfzfvwiGhE9ZaCX2GZBXjlPP7yQygpX1Q7sYFw24y/rzLTp3TGZRJ+ZbaIk0rFrOLtjM3TPK7UUKbX5mXWLskVRIH6f/g/ZcVRV85mcERBkoajAO5nAbJbo0LkxcXv0VQwEBIoLKwgL1zcSDruLX3/YzbqVh3E5FTp0DefaW3p71KOdKfN/2u21W1ZVjdISO3G7M2v10lATpSV20o4U0qChP1HR9ZcwUx17pYvVSxPYuiEFWVZJSylEO2mjn3Qoj+f/vZjXP74c/4ATruXQPu0wBfl5qbALkkiTgZ3qZW0VmflsfegjUn/fgKZpRAzrBoJA5ooduuOdJRUU7D6sG/M2qD2GcfsH0WRwZwr3H/Ho1QiAJLqTIASBhl1bMviThwhqVfsfNUEQCOvXgbB+vtPy583ZTXmp06No2eFQWPTrPkZc0pa2HZvo9iWUJIE+A6LpO6gF8XE5XlmIVpuJAcO8XYV6jJ7Qjl/n7PaKjWmaRmCgvlDmcS6Z0tGnm2781I78+oOnkRCOFWLf8+hw7r/1F6/asOP3DW6gnwquaRqvPr2C1OTCqmSaPTsySNifw3NvTSY8sn52TXk5+tmvqqKRk1V62vOqqsacL7ezemkCZrOEoqhENg3hgSdH0qix/6knqAP2ShezH1lEXm65bj9NcL/TuZwqG/5KYuxJ5SPNpw7Cr0lDyuxOj++F5Gel6xnuzMAdU1vQ7x4qswursjYzlutnnB5HEAUUh04s3KBOGPveiwhncRkFe5NwFOmrI3d55BpM1dSOBZNEYLMwpufM47q837g09hNC+/h2SZ4uO7em6XbjECWRfbsyMJlEZj02DKvNVOWCtNpMNGzszw139KV3/2jatA/zSEKw2ky06xhGz77NarWGsZM70qlbBBaru8Gv1WrCYpW4798jGDGunc/rwqOCaszsGze1EwOGtsRsFvHzM2OzmWgcFsDjz40lMMjKwOEtvVyPFqvEsLFtfSZVHNibRXpqkWeWqOYumF4wV79O8HSI9FGILojCGe0Qly44wJrlh6oaTjsdCmlHCnl99ooaa89Oh5VLEmo0bMdxOGRSkwo8jolmE5M3vk+Ly4feyfCuAAAgAElEQVS66ztFgdD+HZmw+i1C2tbuc1UTid8ux1lUVmPzguoIokhob9+fR4PaYezcLgJUl8zm+94n8ZtliGYTqstFq+vHMPDD+z2UpINiIpi47l02z3qf7I37ECWJ5pcNZsB7s7CEBLL/g/nEvfkz9vwSGnVrRZ9XZxAxtFu9rLG6ttdxBMB8LDbXqVskr398GetXHSYvp5w2HcLoO6hFlWF4+OlRbN2QwobVSQAMGdWKfoNa1Drj0GQSefCpUSQdyiM+LpuAAAt9BrYgINCCqmpENgshM92z/k4UBR59enSNWXCiKHD7rIFcfm03khLzCWlgo037sKprbp7pLlXYvPYIkklEUVSGjGzN9Ft7+5wz8WAeTp32WaqqER+n7+Y8HaZN7847L6322HVKJpGw8EDadz699l8Ai3+L03V35ueWk5yYT6u29ae7t2XdkVMaNnD/99dzjdrCGjDyx/+gqapbaqeGJgZ1JeuvXbr1croIApKfhUGfPlSva/inYvwFLwK2PPghif9bjmJ3VqUnJ81ZhSCKDP7kIY+xjbq1ZuLad1AVBU1RSV+8lfj//kHmmt3kbotHPRbUzt18gGXj/83YP18kcuTp1yNpmsZfyw75TJNXVY3ufU50ywhp4MekaV10x0qSyMBhLRlYSzekL1q1DfX6cRVFgeffnsRvP+zmr2WHkF0K7TuHc8s9A2gcWrvO/I1CA2ikM9ZslrjjX4OYfmsfCvLKCW0SoFtScDLBITbMFsk7exQIblB/2Ymdu0dy5/2D+f6zbZSXOdE0ja69orhj1qAz6h7iq+G0IAjkZpeRklTAysUJ2Ctd9OjbjMlXdKFBw9Pr1mGuZUKOJAkMHaXf4BncO6b6TuIIbBGOYDah6TRO9ry5QOOebRn8+cM07uF7jQa1xygF+JvjKq/khybTdLsmSDYL12b9oivtYc8vZtGQ+yk/mufRILY6jXq05tIdn572+uZ9v5MlfxzQacALkklixv2D6Dc45rTnv1gpL3Py4B3zvGOMVhN33j+IvoNa1Ov9VFWjpKgSq58ZPz/zqS84Bf/3rz/ISCv2Om62SHTo3ISD+3OqPhOSJOIfaOaFd6acloFbv+ow//tka42NogGeeX1Cve4YNVXl6NJt5G49iH9kI1peM8JLgLQk8Sjzu9/p0bhZD8nfypWJ3+EfUb/ddi5GDLHSfwiVmQUIPuprRLNExdE83XNb7v+A0qTMGg0bQMGepBrPJx3K45O31/PyU8v4/ac9lJaccMFUVjhZ8ru3YQOw2sy8/P4Uw7D5ICDQwkNPjcLf34zNz1zV3mrs5Pb0Gei7U8rpIooCDRr514thA7j6xp5ecUazRaJVm8Yehg3cvTcrylwsnLfvtO41aHhLOnWLqFHpZ+ykDvVq2Jwl5fzeeyarr3meXc9+w9aHP+Kn5teSvTHOY1xwm6YM+/b/MAXYMAf7Yw72RzBLCCYRU6Af5iB/TIF+jPz5GcOw1TOGW/Jvjn9U46o6meqoskJAtHfcRFNVjvyyVldjqjqWYN+ZbauXJjDni+24XO6u8YcT8li+MJ7Zb0wktEkg6alFSCYRdFySToeCf4B+Ya2Bmw5dwnnvm6uI252Jwy7TvnP4abvuzjU9+0Vz14OD+eGrWAryKjCZRUZc0pbAICuH4r27zSiKys5t6Vx/R98630uURO5/YgR/ztvHrz/sRq1WW2ixSlx2bf3Ejo+z7bFPKT6QUiXfdDyutvLSp7g28xePGs+YaUNpNqEfmat3gaoSMaIHhXFHOPzdcvzCG9L5gSswB9ZvBqmBYdz+9pj8bXS4ZyrxH/3h0a1c8rfS7o5Jul3HNVVD1enVWB3Jz0L7u6bonqsod/L9F9s9Avkup4LsUvjx61hmPTac4BAbik4KPLjdklbr+Ste/rtgNkv06HPmWXvngz4DW9B7QHNcTgWT2a1AvmLRQSSTiKrzwmM7g12jIAhMubIriqyyYN4+TCbpWHkL3P9/IwgMqt8XqaTvV+jqEqpOmay1e7yaIpv8rERP7I8qK6y98SVSf9/o3sEBCZ8vYtyy13wKnhqcHoZxO8domkbWX7s49PVS5Ao7MVcOJ2ba0DPKjurzyp0Iokj8h/NBENBUjQ4zp9DnlRm640WTRGi/DuTpaEwBSAE2UFWixvamp46EB8D+vVlIkoiL6jVjsGv7UQDCI4Np2qIBKdWUmM1mkUHDW2Hy0XXE4OJBEAQsJ5U79B3UnB+/9q7zslglRk848/T3y67tzqgJ7Ynfl43VaqJT9wif3W3OBJ91aAI1uvr3vPw9qX9scid+HfPgu8rsLBv3OFce/s7oSlKPGMbtHLPl/g849NUSd8sfTePokm0c+GA+41e+4ZG2XxdESaLvqzPo+ewt2LMLsTVp4NUhpDoD3/8Xi0c+hGI/oTEl+Vtpe+sEwvp1ILRvexp08B3bkY4Ja+qu56SWTff/3whee3o5+XkVCIK7OLhNhzCuv+OU8WCDi5CQBn7ced8gPntvI+B2R5pMIt17N2X4mPrJEgwOsdFvcP0m3FQnfGhXslbv8jquOmXCh3b1ed3+9+d7J5doGvaCEnI2xhE+xPe1BnXDMG5nCU1VOfTVEva/9yuOglKiRvei+bQhJHy52MN9KJfbyd95iENfLaGDDxfgcVylFRz6ZilZf+0msEU47e+a7OHKMNksBLYIr9X6Qvu0Z8q2j9jz8hxyN+8noHk4XR+7xquLv8Mhs31TKvm55TRv2ZBuPaMQJZFO3SN1i7IlSfBIeGjYyJ+X3p9K4sFc8nLKiW7RgGYtGtZqjQYXJ/2HxNCxSzhbN6Zir3TRuXskLdt46/idK0pL7JSXOgkND/RZj1md/m/fw8Ih96NUOk4IkAbY6Pbk9Vgb+u4e4/TRYEEQBCqzC+u+eAOfGKUAZ4n1t79O8s9/VQWaBUlEkERdPz1A2IBOTN74vs/5KjLyWNDvHpxF5cgVdgSzhGg2MfTrx30Ki54paUcKefmpZciyitMhuzuGNPLnqZfHExhsZfumFD55ewOyrKKqGiazSHCIjWffnHRBd4o3MAC3fM/Hb6/nwD63i10UBa66sVet3aMliUfZ/dL3ZK/fi39UKF0fvYboSQNqvOaPPjPJ33HI67hkszAt/msCm9fu5fSfjKEKcB4pPpRO0g+rqgqqwa3U6yurEThl5uKWhz5y96c7NofmUlBcCutve53oSQNO6YasK5qm8c5LqykvO/EM9kqZnKwyvv5kC7MeHUafgS3wD7Dw/qtrsVe6EASBkmI7f/6yj+m39T6jImADg/ok8WAuf/y8l6OpRUQ2C2bKlV357vNtHE0tRlHUqmbeP369neAQ6ynrCO2VLuIynMjXXMaQF2YSERVc4/jj9H1jJssnPeHhmpT8rbS8ani9GbbsjXHsffUHShKPEta3A13/Pb3GEMPFimHczgKZq3aCD6kQXwQ2r7nVUdofG3WNoyAKZK7eRfTE2qtR14bU5ELdLhOKorJjSxqyrCIK8Nl7G6mscKJpVGXArV6WQHhUEKMn1H+PSgODurJzaxr/fXNdVW1dXm45B/a5W5gp1b5TTofCrz/srtG47duVwXuvrKmKIWvAwGEtufWeAT4lgpxOhfWrEtm0Nhv19jtptGcH1u27yOjYndxW7dkl+ZPxzQ4mX9GFgFM08a6JxO+Ws3Hm21Whj5KEdI78spZLlr32j9OIM4zbWcASEoBQR4VlX82Oj1Oj+1j1vSM8XeyVLp9fVE3VUBSVg/G5VJQ7vVR0nA6FxfP3G8bN4Lyjqhpff7TFq5GAnvDtcfJyfH8Xy8ucvPvyX17zbV6XTNsOYQzTSYpxOhVeemIJR9OKq66zhndEmNIJ2aUiO1RwVLBswQG2b0rhhXcmY7V5Jpfl7Uhg1+z/kb1xH2gaAdFN6HD3VNrcdEmV10ZxONk86z2PmL6mqMgVdjbd8w6X7frM53NdjBh5p2eB6CkDQSfZwieiQOBJxdZypYO87QcpTc48MefE/u7isGposkLEyB5ntF49Yto09nqrPU5UdAhWq4mCvAqf8nAlRbVsFmtgcBYpyCunovzUAr0nE9Yk0Oe52M2puu52p0Nh2QL90poNqw97GDZwyz3ZK2UPKSRZVikqqGTdqsMe12et3cOiYQ+Q9ucmnAWlOAvLKNyTxOb73ufPgbOQK9zftfydiT7XXbQ/BVdphc/zFyOGcTsLmAP8GP3bc5gCbJgCbXCKXZxks9DhnksB2PfWXH5oMo0lYx7hty63s6D/PZSl5dDv7XuwNQ5G8nO7LARJRPK3MvDjBzEH1H/XCqvVxDU398ZyUqG1ILjrkW6a0Q+AmNaN0HwY8eiYExmRWRklJOzPqfOPjIHBmWK1mXSzeo8jSp6GymKVuHx6d5/jy8ocPnd9J8enT2bT2iO6Lej0cDoVdmxN97x+1rseu7HjaC6F4oR04j9eAIDJ3+ozri8IAsI/TGmgXp5WEITxwLuABHyuador1c7fArwOHD126ANN0z6vj3tfqESN6c016T+R8ut6ytNyiHtnHq7SCs8PnwCixUyfV+4krF8HkueuYcfTX3l8kPN3HGLJyIe4IuF/TIv/moOfLSRr9U4CWkTQ8d5LadS11WmtLz4umx+/iiU1uQA/fwujJ7Zn6lVdPVKhx0xsT3hkEH/O20deThkxbRpz6dXdaH7McEXHNKR95ybEx+V4dCqxWCSuuqknhQUVvPvSao6mFiOZRGRZZdyUjlx5Qw8j2cTgnBAUbKNVu1AS9ufonpdEAVEUkCQRSRK4+qZeNcbb2ncKx2QSUKrZKlEU6NQ9Uvcas7kOewgBgk7qpiLbnRTvT/U5XLU7Ofz9Cro8dBUNu7bCFhZCWTWJHcEkETW2t5eW48XOGZcCCIIgAQnAWCAd2AZM1zRt/0ljbgH6aJo2q7bz/t1LAapTlpbDtkc+Jm3hZgBCe7ej5fRRtLxqOLbGbo2p+d3uoHBfste15iA/Rs17lqgxvvW/6kLC/hxen73CQ4bGYpHo3rcZsx4dVqe5nE6FX77byV/LDuFwyDSNbsD1t/ehU7cInvjXH2Rllnr0+rNYJa69pbcRjzM4Z+TnlvPwjF91Xeg2m4l7Hh1KeGQwoU1OXeemaRpvv7CaA3uzqr4/gihgs5l4/u1JhIV717htWX+EL97fdErVAnB/Px55ejTtO7szJ1VZ4dvAiT5LiAACW0VwVeL37mfdeYglox5GlRXkcjumQD+sjYKYvPF9/KPqr3H0+eRclgL0AxI1TUs6duMfgUuB/TVe9Q8jMLoJI396usYxZan6b5eqolKanFVva/nx61gvfTWnU2HXtnSyMkpqndYMbqN43W19mH5rbzRVqxIOTYzPJT+vwquJrdOhsPDXOMO4GZwzGof51uMTRAFF0Wr9mRcEgfv+bwRL/9jPqiUJx4rQo7ji+h66hg2g76AWbNuQwp4dGTgcMoLgVkho3S6UxPg8t2IvgAYTL+9cZdjA3Sov5uoRHPnpL5/lQhVpeZQmZxLUMpLGPdtyVcoPJP+4mtKkDBr1aINks7DulteozC6k6bg+dH7oqlMqEKguGU1Vkax/391efRi3pkDaSf9OB/Ty0q8QBGEY7l3eg5qmpemM+UcT0rG5br9HQRBo0Dmm3u6TklSge1wSBZIP5dfJuB1HEASEk+IXebllPttzFRfWLLNjYFDfNG/ZSPdzL7uUOkvhmEwik6Z18SmqWx1RFLj3sWHE78tm26ZUTCa36G7LNo0pyK9g55Y0VE2jR59mhIV7J7MMeO9fFO5NonBPMrrbTwESv1lKz9m3AGAJDqD9jMkAxP7nS/a/M6+qmUTxwTQOfbWEqds/1u1mVJ6ey8a73+bo0u2gaYT268Cgjx6gUbfWtfzrXDjUR0KJ3k9Y9f8CC4AYTdO6ASuAb3QnEoQZgiBsFwRhe26utyzGxU6v525B8vcsxhYtJkI6RNNkYKd6uceyBQc8MrQ8ECCkYf10FomOaei1aztORNO6G08DgzPhutv7eOnLWawSI8a1q7WMUNKhPL77bCtffriJ3bFHa0xUqY4gCHTsGsFNM/px3W19qtqNNWrsz+iJ7Rk7qYOuYQOwNgjk0h2fEtJeXx1Cdcq6rbvK03PZ98bPVYbNPdaFs6iM2Ke+8BovV9j5c8C9HF2yDU1W0BSV3E37WTT0AcpSs2v9rBcK9WHc0oGTtRqaARknD9A0LV/TtONZEp8BusEjTdM+1TStj6ZpfcLCwuphaX8vmo7tw7Bv/o1/01BEqxnRaqb5pYMZt/yNeknASDyYy9zvdvo87+dvoUOXiDO+D0DT6Aa069TEqyO7xSJx1Q09fVxlYHB26NA5nMefG0uHLuHY/Ew0iQhk+q19uP722jXwnvvtDl5+ahkrFh1kzfJEPnx9LW+9sMpnuUx9IwgCLa8dhaSTFGIK9NONx2esiNVVG9EUlfSFW7yOJ/24GmdxuVfGpWJ3EvfOvDNY/fmhPtyS24C2giC0xJ0NeS1w3ckDBEGI1DTteNHWVEC/IMSAmCuG0WLaUBx5xe5SAv/669G4YuFBj6zGk7H5mXj8uTE+C7dPh/v+bwRzvtjOhr+SUBWVkIZ+TL+1Nz36/j31yQz+3rTpEMb/vXBJna9LTS5g2YJ4jzi1wy6TEJfDpjXJDBl1blx2He+9lIMf/YFdVtBk91pEq5mg1lE0v3Sw13jJz+pbucPqrUCSu3m/xy7vOKpLJqeawvjfgTM2bpqmyYIgzAKW4i4F+FLTtDhBEJ4Dtmua9gdwnyAIUwEZKABuOdP7XswIgoAtrEG9z1tY4LvoukefZmiquztDaA1FrHXBajVx6z0DuHFGP5wOGT9/s1ECYPC3Y/O6I7h0xH0dDpmlCw4weGSrc/K5tjUOYWrsx+x45mtS529AtJhofeNYejx1o4fy93GiJ/ZH0wkNSDYLbW8Z53U8qE0Uks3i0RMXAFEguE1UvT3HucJQBThNXGWV7h6SAkSO7KmreH2h8ee8fcz/aY/X7s1kEhElAUEQUFV35tisR4cZsTEDA2DOl9tZtuCAfi6H4E5WeeSZ0RekEkbK/PWsuf4lNFVFdbgwBfoR0j6aCX+95dX8oTK7gF/a3Oi1e5P8rExc+w6hvc9cTLY+qG0pgGHcToPD369g411vIRx7W9JkhUGfPUzr6aPP88pqprzMwf/N+oPSEkeNwXBBgMAgKzMfGoLFYqJVu9Ba61wZGFxsJBzI4Y3ZK33WqUmSQNuOTers8nQ4ZOL3ZqOh0bFLuFc/yfqiIiOPw3NWUplVQOSIHjSd0A9R0lcnz1q3h9VXP4dSYXf/EGgw8OMHaT191FlZ2+lgGLezRGHcERb0u8dLTVfyszJ1239p0Cnm/CyslhQWVDD3251sWpN8ymwvs1lCMrl1rmY+OITufZqeo1UaGFw4aJrGJ29vIHZLqs82WmazxGsfXUqjUN81dSezdcMRPn9/U1WMW1FUbrt3IAOHtayX9R7+djlx785zCyWP6UX3J28gKKZ2yWKqopC/PQHF6SKsX4cLrtattsbNeB2vI/Ef/Y7qdHkdV10yBz7644zmVmWFtEVbiP9kAXnbD57RXL5o2MifYaPb1KwycAyXS8Fe6aKi3MkHr68hO7P0rKzJwOBCRhAE7npwMDfe2ddngobJJFJSXLtm4RnpxXz27kYcdpnKCheVFS6cDoUvP9hEemrRGa930z3vsOnedynYmUh5SjaJ3yzj954zKDmcceqLAVGSCOvfkYih3S44w1YXDONWR8pTc3Sbk2qyQrmPDiO1oTghjZ9bXMua6c+z9eGPWDziQRaNeBBXef0XPG/dmOIzscQXiqKxemlCva/FwODvgCAIDBnZmsAgfVFgRVWJrGWMevWSBGSd3xBZVlm56MxeaksSj5L4zTKPuJkmK8illez4z5dnNPffDcO41ZGIkT29Cq0BTP42IkedXv2Wpmksn/QElVmFuEorUSocyBUO8rYcYNujn5zpkr2QTiPdX5FVsjNL6n0tBgZ/F0RJ5OqbenkoZYC7GHzyFV1qHTPLyy3TbXCgqhp5ueVntMbM1bt0hZI1VSVj2YUX5jmbGMatjrS7bTyWYE8xUkESMYf466bX1ob8HYeozC7waq2jOFwkfrO0Vi7EujBgWEyNCSJ6rheLRaJdh5rVwg0MLnaGjWnDjPsHExEVhCQJNA4L4Prb+zL1qq61nqNDlwgvAwluI9mpi3dLrLpgDvLzKZRs+htkdNcnhnGrI5aQQKZs+y8xVw5H8rMg+VmJuXo4U7d9hCXk9OrDHAUlCD6ylxS7q6pgs75o3S6MkePaeRm4wGAr/3l1PP4BFg8DJ4gCFquJYWO9VYYNDP5p9B3Uglf/exlfzruBtz6bxohL2tapzm3Y6Nb4+Zs9GiYIgtst+ccve5n9yCJ2bz9awwy+iZ6sL5Qs+Vlpf9fk05rz74qRLXkB4Cgo4adm13gXTwINOsdw+V7vPnBniqZpHIrPZf3KwyQl5lNYUIHLKdOqbRhjJrZj5eIE4ve5+8l16hbBzTP70yRCv+u5gYFB7VAVlT07Mti2KZWkhFyys0pRVQ0BwSN72WKVuPHOfgwbU/cXyqPLtrNq2tMgCCgOF5LVTJNBnRmz4EUky9kpNziXnEvJG4M6INudlCVlYAtrUNWFxNoomM4PX8X+t+dVScYDSP5W+r9bawm8OiEIAu06NmHdykSyM0uqUpwP7M3icEIu/35+LDGt3c1dpVMoiRsYGJwap0Pmlf8sJz21CIddRpIERElEkvAqMXA6FH78OpbBI1vV+fvX9JI+XJ3+Mym/rMGeX0LEsG6EDej0j+sOZBi3c4Smaex97Ud2v/gdCAKqUyZqTC+GffsE1gaB9HruVkLaNmPPqz9QmZlPox5t6PXC7YQP6nzW1pSXU8amNcm4XJ6ZW06Hwg9fxfLUy+PP2r0NDP5pLP59P0cO56McSyZRFA2luqT3SciySk5WKZFNQ+p8L2uDQNrdMem013oxYBi3c0TCF4vY/fx3HjuzjOWxrJjyJJPWvYsgCLS56RLa3FT3xq56HE0rYvXSQxTkldO5eySDR7TC5ufpkkg8mItkEr2MG0ByYn69rMPAwMDNykUHqwxbbVAVFf+Av2+d2fnGMG7niF3Pfeth2MCtw5SzMY7vGkyhycBO9HrxdkJ7nXn/tvWrD/PNR1uQZRVV1di3M5OF8+KY/cYEghucyJgKCvbdC8/f3/hSGRjUJ+Xl3jF1X4iiQOv2YYQ0+GdlONYnRjClntBUlfQlW9n7xs8cmbcWpVoXk4qMPB8XarhKKji6dDuLhz1Aro4Sd12orHDy9UdbcDqVqgC1wyFTVFjBz//z1HLr2CUcq9X7/cZikRg14cJokmpgcLFQXdvwZPwDLNhsJiwWCZvNRFhEIKPGt2PbxhQKCyqqxqUkFfDOi6u5/7ZfeO6xxezYklanNWiqilxhr/fyogsRY+dWD9hzi1g0/AHKj+ah2F2YbGZMAX5MWPM2IW3d2mUB0U0oT6lZzVaucLD1kY+ZtO7d017Lvl2ZSJJA9QZhiqKxfVMqd9w3qOqYKIk8OnsMrz6zHJdTRVM1VE2ja88oplxZ+7odAwODU9O5WyTbN6d6HZckgdtmDSAw0EpGejGCIPDrnF18+eEmwB17GzmuHX0HNueN51a6deU0KCqo5KO31nH59O5MvKzm2LwqK+x4+isOfDgfpcKBX3hDer98B21u1A+DFO5LZttjn5C9bi+mABvtZ0ym+5PXo6kaif9bxpG5azAF2Gh3+0Sipwy8IJNVDONWD6y/801KDmegHdN8crlkXOV2Vl3xDJfvcafx95x9M5vvfRe5wlHTVORtjT+jtbhfyPQ/aBreb2vRMQ1554sriduVSXFxJW3ahREVXfcAtoGBQc1Mu747e3Yc9RA9FUVoFOpPr37RSJJImw5h3H/rL5SXebow1yw/ROwm78bNTofCb3N2M3JcO/z8fKf5b7jjDZJ/WYNy7PenIiOfjXe/A4JAmxvGeowtik/lz0H/crfw0jTkcjv73vyZ7PX7cBSWUHoooyrEkrlqJy2vHsGQLx49o7/N2cBwS54hcoWdo0u2Vhm2KlSN0qRMig+lA9D25nH0evkOLCEBiDW06TEF1d3HXpBXzuqlCaxZfojmLRui6PStE0WBjl0jSDiQQ2WF5xfHZBLp3qcpw0a3MQybgcFZoml0Ax5/bizNWzZEFAUkSaRnv+Y8/eqEqnT/XdvSdZNOnA6FgvwKr+MAkkkk+ZCPsAdQkZlP0k+rqwzbcZQKB7FPfO7lotw5+xv32JOOK5VOsjfspTg+zSN3QC63k/zTanK3ndlL+dnA2LmdIXKl752YaJJwFpVV/bvzv6bRceZUylKyWTjkfuw5hR7jJT8LHWbUrYvA7z/tYcEvexGEE2KjfQY1J3ZzKrLLnVBitkioisq+nRnE78tGllUmTevMZdd0q7M7weVS2Lk1nbzcMprHNKRTt0iPTgsGBga+adMhjOffnozDISOJAqZqcbjiIrvuy2lNqKqGXw0JYEVxR5BsFlSHt5pJZUYBisOFyXbi+ux1e9BU/ebwmuZduiDbnaT8tp6wvh3qtO6zjWHczhBro2D8m4ZRlpzpdU5TNRp2beVxTDSbCG7TlHFLX2XJmEdQnTKqrIAA4UO60uOZm2p97/h92fz56z6vVP7tm1KZ+eAQ9uw4SkFeBcmJeZSXKSiKVjV20W9xNA4LYNjo2ndAyEgv5qUnl+JyKricCmazROMmgTzx4iU+u6UbGBh4o5fIBdC6Xai+rI4ADRv6UVriQJY9v+9BwVZiWjfyea+A6CaoTn2hVVOADcnq6UmyhYZQmVmgswbBq/+t+7CAaPKdLHO+MNySZ4ggCAz88D4vpQDJ30rfN2Z6vBGdTKPurbnm6M8M/eZx+r15N5PWvcu4Ja/WST9p5eKDuuKJiqxyKD6H270NBEwAACAASURBVO4dyOQruyC7VK/PpNOhsGDuvlrfS9M03nlpNaUlDuyVMoqiYbfLZGeU8M3HW2o9j4GBgW9atmlM2w5hmC3VlAcsEjMfHkpksxCsNhOSJGCzmQgMsvLgkyNr9MCEtI+mUY/WCGZPgyr5W+l03+Ue15YmZRA9dZCu8olkMSPp/J6JFhMtrx5Rxyc9+xg7t3qg2fh+TFj5Jjuf/YaCPUkEtYyk+5PX02x8vxqvkyxmWlw25LTv60scUVU1dmxJY+e2dFxOxetN7zhFhfo+fD2OphZRlF9J9ZwUWVbZsSUN2aV4uVgMDAzqzgNPjuLHr7fz19JDKIqGIEC7Tk0IjwziubcmcWBvFinJBTQODaBnv2gsllN/78bMf56Vlz9N/s5ERIsJ1e6k1fRR9HjmZgAqsgpYNe1pCnYnIZgkFIcLQRKR/KzukIck0vG+aex+4VvPiQWBjvdeRsMuZ64gXt8Yxq2eCOvfkUsWvXJO79mjT1OSEvI8sq+Ok5NVpnOFJ3Vp61NR7kKU9N8OVU3DJauGcTMwqAcUWSF2c1qVt0XTYP/uLGY/vIhXPpxK5+6RdO4eqXttaYmd2M1pOB0yXXpGEdXM/R23hTVg0vr3KD6UTnlaLg07t8AvvNGx+TWWjXucogMpHgokos1Ki8uH0PKq4YQO6MjcmOu8FAcESUTRieVdCBhuyVpQmpTBtsc+YeVl/2HPaz/iKLgwRDtHXNKWwGArUg3abL6wWCSuurH24qotWjfyGehuEhFUYxqygYFB7Vm78jAV5U4PlQBV1agod7J25WGf121am8yDd/zKnC+28dP/dvD0Qwv56r+bPbIhQ9o2I2pUzyrDBpAfm0BpUoaXtJZid5C5cgfRkweStWoXoo4slyYrJM1Z6XlM08jeGMfBzxaSuXqnbnLKucDYuZ2CtMVbWH3FbBSXDIpK6p+b2fHUl/R85iY6P3y1z5hafVNcVMneHRkIokD33k0JDLLi52/hubcm8fvPe/lraYJuj8iTEUV3RmWDhn5cd3tvuvaMqvX9rVYTV93Qk7nf7TwR5xPcRvLmu2p2vxoYGNSe/XuydGPpTqfC/t2ZjJvS0etcQX4FX3ywCVc1L86mNcl06hZB/yExPu9XdiTLp8BpZU4RAKrT5bOriXqSUXQUlrJ0zKMUJ7h3noIo4B/ZiAmr38I/KtTnGs4GhnGrgewN+1g55Um0k7fiqoqmwo5nviZ57homb/x/9s47PIpq/eOfKVtTSSWUQAi9d5QiVURBsSHYC4r9Xsu19y6Wa9erPyv2hoIFBRGk915CL2mk183Wmfn9sSGw7Gx6AGU+z3Mfyc7MOWdzs/ue85bv+wayPbRGY2Mw58etfP/5BsTKP0BV1bhqmr/XU0SklSuuH0DalhzS9xeFHEOSBMZf2I1xE7thDzPVS1Fg7LldSGgewezvNlOQ56BNSgznT+lJuw7H94/WwOCfTGycHVEM7O8G/s1pbHyY7jMrF+/XNT5ut495v6RVa9ya9WiH6tXPpoxI9W+AW4ztr9s0WZBEks87onq0ZOqLFG3dF5CdWbY3mwWXPMn4Ja+HXENTYLglQ1C2/xC/n3lPoGE7GlWjdFcG29+Z3aTr2JWWy8wvN+L1qrhdPtwuH16Pwoz3VpGVUVJ1X2rH2GrrzURJZOioVMLCzQ2Syuk9oBWPTj+b1z68mLseGWUYNgODRmbkuI668W1JEhgysl2Q0QNwlLvxhfDcHKt2cixRnVrTfETvoExIyW6h/7PXA2BLaEafJ64JyKKUrGYssZH0e2YqAN6yCjJ+XRVUdqApKgXrdlGenlvtOhobw7iFYMXtb+h2xj4axelh7+fzq72nocz/daduwojiU/lr3i7A7+PuM6BVyNibySQx5Zp+JCZFNulaDQwMak/a1hyevv83bpzyJffc9CML5+1i07pM/vvUn1XJJIIgYLH4U/9lk8TTD/zOTZd9xTcz1gXEwLv2bI7Fqu+IkyQBt1v/ZHaYUd89TupVZyJZzYhmGXuLWIb83920ueBINnePeyYz9pfnaHPhMOJP70qP+y/lgi0fEtYqHvAbNyHEBls0ybgLjm+uguGW1KF0TxYZc2pXu9XUxYvFRRVB6ffgd02WFDk5lFnKK88soKiwojKm5s+uEkVIaB7JoGFtGT6mfUh3hoGBwfFn68ZsXn1mQdXG1XWojE/fW42qqqgB8lsaqqYiSSLOCn9WolvxMe/nNEpLXFx/u98l2KVHc1I7xrFzW25Q6U92RimvPbuQe58YE3I9ss3CkP/dxelv/Auvw4U5KkzXw9N8eC+aD++lO4ateQzmqDCcOocCTdOI6pxc3a+k0TFObjrs/OBXXYNyLJLdQvtrm7Zbda++LYMKOgEsVpmuPZN49qHfyckurXJZahqYLRI33jmU6W9P5MJLexmGzcDgJOOz91cHeWR8XuUYw+bfqHo9atC9Ho/CikX7KCl2Av4T3t2PjCIsIjjBzedT2bk9l4yDxTWuSzTJWKLD6xW6EESRga/eGlQALtst9H3muuOWfHcYw7jpUJGRpyszczRyuJW4/p3oOPXsJl3L8LEdCA83Ix3lg5dlkWYxdqw2Gbfbp6s+8usPW5t0XQYGBvXD51MD4uX1RTZJZKWXBPwcKr4mSUK1CWf1wVNSjiMjLyDVv93kkYz+/gni+nfCFGmnWfcUhn18H91uv7BR564NhltSh6SRfTjwwxJ/y4djiO7Wlrh+HUm+YCitJ5ymW/vRmNjDzDzx3/HM/GIDa5anI4hw+hkpnD+5F0sX7kEJoT5SmF979REDA4PjhyQJmE2Sbiy9Lvh8KjFxgV6ZqGgbBXmOoHs1DeISGseD48ovYfG108matxZBEjFFhjHo1VtpN3kkAC3PGkDLswY0ylwNwTBuOqRMGcmGpz+jIiPvSIqsIGCOieDsBf/FGnd828JERdu49pbTufaW0wNet1hNIQ+YySnNjsPKDAwM6oogCAwbncqiP/bgPbZVlkBASEQQQDaJCAgBxlCSRdp1iCUxKSLg8QkXdefLj9YE1MmJokCzWDvtO8U3eO2apjFn1F2U7sio+m5UnB6WTH0RS0wELc/s3+A5GgvDLamDbLNw7sq3SL1yDHKEDcluoe2k4Uxc+78mM2wFeQ4+e381j939C29M/4vdaXkh7/W4fbzw2Dw+e2+VrmqI2SJx4WW9m2SdBgYGDWfKNf1o3zkes0XCYpGx2mSaxdq5atpAIqIsWKwyJrNE67bNeOKl8Zx2RltMJhGb3YTJLNG+Uxz/un9E0Lgjz+rA2AldMJklbHYT5sox7ntiTKN0yz7010bK9+cE1cUpFW7WP/Zxg8dvTIRQVecnmv79+2tr1qw50cs4LmQcKOLp+3/H41X8bsZK5Y8rrx/AGWd2CLr/s/dXh1Qkad4igqtuHBRSe87AwODk4cDeQg7sLaRZrJ1uPZsjSiKqonIouwyLRQ5IBispdpKVXkJMXFjQie1YHOUeMg4UERltrZOGbE2kvTObVf/5H4pOH0tzs3AuL5jVaHOFQhCEtZqm1XhENNySdcSRkceGJ2eQ/ssK5DAbnaaNp9u/L0I01f9XOePdVTidR4mPav6kkM/eX8OgM1ICej9pmsaiebt1DZvFKnPjnUONwmoDgxOI0+ll8fw9bF6XSVS0jVFndwz5mWzTLoY27QJ7sYmSWCV4fDRR0Taiom01zu/1KmzZkEVBnoPWbZuRmBTZaA2FI9q3QAhRTxuRcnJtqA3jVgcqsguY1WcanhJHlRTN+sc/IeuPdYyd83y9jv2q4k/T1UOUBHan5QWcwjQN3CEaD4qiQFlp6M7gBgYGTUtpiYvH7v6F8jI3HreCIMDKpfuZdGVfxk5o+k7VWemVDYW9/obCskkiLiGch54dS1h49Q2FFbcHb7kTS0xkyO+yFqP7YktoRnmFG+2okIhkt9D70do3Wj4eGDG3OrB5+ld4SysCNNaUCje5S7eQu7T2jT8DEISQuypN86f9H40oCrRKjta93+dVadchtn7rMDAwaDAzv9xASZGrKqFDq/TCfPPJupD9FxuLqobCZUcaCrsrGwp//E5oUQqvw8mSqS/yebOJfN1yMl+3msyeL/WVlwRR5OyF/yX+tK5IVjOmCBumqDAGvnxzgMbkyYBxcqsDGXNW6gqM+ircZP+5nsShPeo8pigK9B3UmrUr04MKOGVZpH3n4Ayny6f255WnFwRkT5ktEqPP7kREZNOKOBsYGIRm9dKDukleoiSwaV0mQ0emNtnc6QeKKS6qpqGwTw3aLAPMv+BRcpdsqZIbdGYXsPSGl5HtVtpMHBJ0f1jLeMYvfg1HZh7uwjKiOrVGMp98La8a5eQmCMI4QRB2CIKwWxCE+3WuWwRB+Lry+kpBENo2xrzHG3Mz/SCuZDGFvFYbrpw2kGYx9iptOJNZwmKVuf2+4Ug6rSi69kzinsfH0LFrAlabTELzCC6f2p/JV/et9xoMDAwaTqjIhACNkq1YHU6HJ6QXSFU1fMeWHQBFW/eTu3RrkI6uUuFm3UMfVDtfWMt4Ynq0OykNGzTCyU0QBAl4CzgTyABWC4IwW9O0bUfdNhUo0jStvSAIU4DpwOSGzn286Xrb+Sy7+dXg4m4BUi4ZXu9xo6JtTH97IquXHWB3Wh5xCeEMHdmOyGqCxx27JvDQs2fVe04DA4PGZ9Cwtiz4fVeQuIKiavTq27JeY2qaxt5d+TjKPbRrH0d4pH7srG37WN1TY3ReNp12r+frZp9iiY2k2x0X0/3uSQiiSNHmvYiyhF45eenuzHqt92ShMdySA4HdmqbtBRAE4StgInC0cZsIPF757++ANwVBELSTtQ6hEndxOQd/XIK7sAxN0yjeso/wlCRKd6WDICJKIpqqccanDwR0tq0PJpPE4OHtGDy8XSOt3sDA4HhzwZRebFqbSUmRC7fbhyiCLEtcNrV/SKNUHVnpJbz05HzKy9yIooDPq3LWeV24+IreQSdBvYbCzfKy6L5qPpKioALO7ELWP/EJpbszGfLuXUSkJIXslG1r3rDvtBNNYxi3lkD6UT9nAINC3aNpmk8QhBIgFsg/+iZBEKYB0wCSk4+vgvSxpP+6kgWXPIEgCPgqXEf82KKAZJJpNeE0Wo0bSJsLhmJpgEvSwMDgn0N4hIWnXzuX5Yv2sXldFtHNrIwY25HWbeuuGOTzqTz38FxKS10BcbS5P2+nRasohowM3giPPbcLCUkR/PTtFvLzyum+Yj2SEnguUyrc7Pl0Hn0eu4q4gZ0JT2lOSVp6QKKcbLfS68HL67zmk4nGMG56Tt5jT2S1uQdN094D3gN/EXfDl1Y/3EVlLLjkCZQKnbR6VUNxe8n4eQWnvXZbkxo2j9uHJIu6cTcDA4OTE4tFZsSZHRihI8BQFzaty8Tj8QV9U3rcCr/8sEXXuAH07t+K3v1boWkaH8v/071HNMvkr9lJ8nmDOWvui/x50WMUbtiNaJJRvQrd7r6YjjeMb9D6TzSNYdwygNZH/dwKyApxT4YgCDIQBRQ2wtxNwoHvF9Uc/BUFDs5aRuebzm30+dO25DDjvVVkZ5QgigL9T0/mqhsHBtWpFBVWMOubTWxcnYnFKjNqXEdGn9PJMIYGBv8AivIrgjKoD1Nc6KzxeUEQMEeF4SkuD7qmqRq2RP9p0t48hglL36BsXzbOnCKadWuLKcKuO6a7sJStr33PgZlLkMOsdL75PFKvGNPkAvL1oTGM22qggyAIKUAmMAW47Jh7ZgNXA8uBi4E/T+Z4m7vYEdQqPQhV0y0LaCj79xTw8lPzq3zmqqqxevlB9uzMp3O3BHw+jYFD2tA2NYZH7/qFCocHpfID8O1n69m6MZs7HhrZ5JlZBgYGTUvb9jEhP8dtU2sXD+t8y3lsfeX7QLksUcDWvBlxAwOLyiNSkqpVGXEXljKr7404c4pQ3X5FpRVb95P+8wpGfvOo7lq9DieHFmxA0zSSRvbBFF6zwkpj0WDjVhlDuw34HZCADzVN2yoIwpPAGk3TZgMfAJ8KgrAb/4ltSkPnbSxURWHfl3+y4/1fUd1eUqaMJOH0LogmqUbj1XrCaY2+nh++2hTUCkPxqeTllJOX49+BrVyyn4goCw6HJ2Bn53ErbN+cw54d+br1cQYGBn8f2nWIo01qDPt25QfI7ckmkfOn6HfDPpY+j11Nyc4MMn5egWCSQANrfBRn/Ta9zhvgLa98H2DYAHwOF5m/rSJ/VRrxg7oE3L/3qz9ZesPLCJWeJM2nMPjdu0i9PHRH8MbkHy2c7HO6OfD9Ikp2ZhDVqTVtLjojoBuspmn8edFjZM1bW5XeL9ksRKQmEdG2Odl/rsenE3eTw6x0/feF9Ht6aoPWp8e/r/uuVi6HUAgCXHBpLyZe0jPoWkGeg/zccpJaRlZbZmBgYHBi0DSN/NxyBEEgNj4Mj9vH15+sY9H83Xg9laLqJgnZJHL5df0ZOrp9rcYt3Z1J/tqd2FvEkjikO4JY99DFD92vo3jbgeALokDvR66kz2NXV71UvG0/swfcEiSwLFV2XGnWPaXO8x/mlBdOLt2TxS9DbsdX4cZX7kQOt7H6nncZv/T1qqP3oYUbAgwbgOJ0U7Ynm47XnU2LMf3Y/vYsPCUOwlsnIIgC9lbxdL75PFqMbpqC6dj4sAYZN9kkYbcHtnN3Vnh468XFpG3JQTaJ+LwKg4a25dpbT9dVLDAwMDj+7ErL5d1XllJS5EQDYuPs3HTXMC67rj9rV6RT4nX65bw8Ch6PwifvraJZXFitOoBEtm9JZPv61dkdRrbrqx+Jsoxst6J4vOSv3oEgCuz5Yr6u50v1eEl7Zxanv3VHg9ZSG/6xxm3hlKdw5ZeA6j+Z+sqdKBVuFl72DOcufxOAg7OX6XbbVpxu9n2zkAnL3qTrv45ve/RzL+rO2y8vDmg2WFcGDm0T8PPbLy9h+5ZD+LxqVXPEVUsPEBZh4bLrTp7mggYGpyr5ueW8+Ph83K4jBuFQVhnPPzKXS6/tj8vlDWpM7HErzP5283Frb9XppnMp/tcBf2nUUQiigCkqjC8TL/KLaWqguDwBpQWH0RSV8oP6QvGNzT9y216RlU/Rln1Vhu0wmqpSuGE3zhx/oqZkNVf5g49FstW94LIx6DOwNRdf3huzxd9sUKrmZCVJAmazhCyLmC0SJrPEtDuGBLTFKCqsYPumbHzHtMjxeBQW/L5TV5LHwMDg+PLHrzvw+YKLqb0ehflzduBy6cf/D2WVNvXSqmh/9VhantUfOczqF3w3y0hWMz3uncyqu97GW+LAW1qBt6wiZL6CZLOQNLLPcVnvP/Lk5nW4EGUpIPB5GFESq05r7S4bzbbXfwjyC8thVjqdwBqPs87ryvAzO7B/TyGyWeSZB34PSgkWBBh/YTcGDmnL1o3ZWG0m+p+WHKSCUJjvQDZJuv3fNFXD6fQSYTr50ngNDE4l0vcXBUl2ASiKRvqBYp2qYD/HMylalCRGfvc4eSu2kfHbakxhVlImj2TT81/qftdWLbDyyClIIuZIOx2uO/u4rPcfadwiU1sgh9t0XY7m6HDC2iRSuHkvrrwSuvzrAra//gOqx4umash2Cy3HDSTlkhHHf+FHYbWZ6Nw9EYAHnx7LC4/9gc+nomkaoiTQpXtzJl7SE9kkVat+kJgUGXRqO4zZIhMWZta9ZmBgcPxITokhbUuO7ulNU0Mn/ZWVuHG7vFisx0e8WBAEEk7vRsLp3apeK92VEdDb7WjsreJw55cA0PrcwQx8+SYs0eHHZa3/SOMmiCJD3r2LhZc9jeL0+HcOgoBkM9PnyWuZ1Xsa5fsPIVSe7lIuG4U1PhrV7aXN+UNJHNbjpKoT69AlgTc/vYR1K9MpK3HRoUsCKe1r17ctPMLCsNGpLFmwJyCOZ7ZInD+5J6JR8G1gcMIZc04n5s/Rd01WhySJlJa4iT9Oxk2PhCHdyFm2FfWYzgKSzUyrcwbR857JRLRrcdzX9Y8uBchbncamZz+nePtBoru1ped9U1h46dM4DuQGiIXKdgsDX7mFTjdMaOiym5SD+wr56bstHNhbSPMWkUy4qDsduybU+JyqqMz8ciPzfknD61Wx2UxMnNyTM8d3OqmMuIHBqUZWRgkLfttJQb6DuIRw1q9KpyDPUSXMUBNWq8ybn16CqZrQQsZvq9j0/JeU7z9EbN8O9H70KmJ7166EoDY4cwqZ2eVaPCUOjs16kSPtaB4fzYf3ZOS3jzdKEXdtSwH+0cbtWLIXbuCP8x7GVx6cah/eLolJuz9r1Pkak+2bD/Hfp//E61Gq/n7MFolrbzmt1p0EVEXF5fJhtZlC9n0yMDA4Pqxcsp/3X1+GT1FRFc2fRGYzcdnU/rz/+vKqzOZQmC0S4y/szvmTg2taD7P9rR9Zc997R+p1BQHZZubMX5+n+Rmhn6srxWkHWXbzK+Qu2VJ5cBACDJ1oMZE8cTAjv3q0wXPV1ridUj4px8FcQkVmnYdOWqlLNE3jo7dX4HErARsjj1vh0/dW19qVIUoi9jCzYdgMDE4wbrePD95YjsejVCWLedwKZaVu1q3MoHOPREzmwNPY0dnR9jAT50/uycRLeoScw+twBho2AE3DV+Fm+W2vNer7ie6czDkLXuGSzG8QTHLQCU51ezk4axnuorJGnbc6/pExt1DE9GkfMjjbrGvb47uYOlBe5qYgz6F7TVVVMg4U0Ta1djE4AwODE8/2zYcQpOBNpqpqrFuVztufTea7z9azcO4u3G4fLVtHc/nU/nTpnojT6cVmM9UYLy9cvxtB1ndXlqSl43U4MYU1rlKRt7gcyWzCp6PNK5pkXHnFx61F2Kll3Hq0I2FwN3KXbAloqy7ZLPR77voTuLLqkU1SUAHnYVRVw2I9pf5vNDD4+6Pp9wEDv6fGbJa47Lr+XHptPzRVCzBkx3YHKSl2smbZQdweHz16t6jKnjZF2ENmMQqigGRu/CSU8DaJ1VzVCEuu7nrjckq5JQHGzHqa9teOQ7KZQRCwt4hl+GcPNJmcVmNgs5no0iMx2J0oQGSklZ++3cIzD/zGd5+tp7io/tJdBgYGx4cuPRJRdAyPKEKfAa2qfhYEodoT2tKFe7n7hh/46uO1fPfZBp68dw7/9/pSNE2jWc92WBODy4REs0zy+UMQTY2/KZYsZnref2mQVJccZqXn/ZcFaPs2NaeccZNtFpKG9/IHVsOteMudLLrqefZ+vaBB43rLnWx/ZzZ/TnqClXe9TXHawUZasZ+ptw+mWYwda+UpzWKVsZglSopdLPtrLzu35zFn1jYeuG0WhzJLKS9zU+Hw1DCqgYHB8cbrVfB6VK6+aRBms8RhDWOzWSI8wsql19ZOEq8w38FHb6/A6/VrTSo+FY9HYfXSgyxftA9BEBjz41NYYiORw20gCkhhFsJTkjj9rX832fvr+cBl9Jt+A9aEaBAErAnR9Hv2eno+cGwntKbllMqWBCjefoDZ/W/WVas+b807RHdpE+LJ0DhzCvlp4C24C8vwOVwIsoRokhny/t2kXjq6sZaOz6uwblUGGQeKiIm18/mHa4I1KAWwWCR8Pr/GW2qnOG7412Aio6z8Nmsby/7aB8Dg4SmMm9gVq+3E1ccYGJxKeNw+Pnt/Ncv+2oemaoSFmxkzvjOF+Q4K8hx07dGcM85sH+R2DMWvP27l+8836Io0tO8UzyPTxwHgzC3ij/MepmDtTkSTjGiS6f3olXS7a1KTlwKpXl+jnxBP+a4AoUh7Z7a+WrXXR9o7sznt9dvrPOaa+/6PiuzCKqFQzaeg+BSW3vAyyecObrQGfbJJYuCQNgwc0obN67MqO24fY9w0cLuOvLZrey5P3juHiCgreYfKqmS4fv5+K6uXHeSxl87BbDbktwwMmpo3pv/F9s05VSn+JcUufvp2M9PuGMKAwfXYVFd4Q6oPVVQc8dr8dfkzFG7cg6aoKIoHxeVh/WOfYIoKo9P1TSsz2BSuz1rPfcJmPkGUH8jRV6v2KZTvPxTwmiuvmMJNe/A6qo9j7Z+5WHdMUZbImre2YQsOgSQJIfXmjkbT/B+CnKzSAH1Jr1chL6eMlYv3N8n6DAwMjnAos5TtW3KCatc8HoVvP1tfrzG79UrSTSaTTWJV3K5kRzq5y7YFaT/6KlxseHJGveb9u3DKGbfmw3sh2YOP/ZLdQvMRvQHwlDr44/xH+Dp5Cr8Ou4MvEy5i7cMfEtKFW41rt6ncvh27JKDWcmyfT9VVPHC7FdaubNzYoIGBQTAZB4uRQySG5GbXr/arU9cEOnZJwGw54nmRZJGwMDPjJnYFoGRnOmII9ZKKzPwm+346GTjljFuH687GHGEPaHUjSCKmcFuVWvWfFz1O5u+rUd1evGUVKE432179nq2vfKc7ZvLEwbqtc1SvQosxTZOFWVrqrlZQ9WhCFW0LAoTX0r9vYGBQf+ISwlBUfReipsHWjdl1HlMQBO54aCSTruhDi1ZRxCWEMfrsjjz16gQio/zZipEdWqGGUDqxJ8X+o+X3TjnjZokOZ8Kqt/2psGbZnxY7cTDnrn4HS3Q4pXuyyF22RfcYv+m5L3R3Ov2nT8MaH13VA06QRCSbhUGv34Y5MqxJ3sf8X3fU+uRmMkmYzMH/V5vMEiPGdmjspRkYGBxDm3Yx2Oyh0+Dn/ry9XuPKssjYc7vw3Jvn8fJ7F3L51AEB/RyjOycTP6gzoiUwcUwOs9LrkSvrNeffhVMuoQQgvHUCo759XPda2Z4sRLPJ303gGNyFZaheX1DxY1jLeC7Y9hE73/+FrD/WEdY6ni63TCS2T+Mbjv17Cvj5+61sXJsRMpgsSSImk4imaUREWbnt3uFsXJvJz99tQasM1AkIjL+gG+07xzf6Gg0MDAIRBIFefVuwaP4e3evFhU1Xnzr6x6dYfM10Mn9bhSD7v/J7PXQ5naad3ELxDeWUNG7VEdWpdcjGe9aE6JDZP5bocHr87pRmawAAIABJREFUZzI9/jO5yda2YU0Gb724KEA8+Vhkk8g553el/+ltkE0iLVpFIQgCKe1jGTIihbUr0wHoN6g18YnHRwbHwMAAevZrycqlB3Af01VblkW69UpqsnnNkWGMnvkk7sJSnLnFhLdtflyLqU8UhnE7hvA2ibQY25+suWsCJLpku5U+j11VLx915tw1bH7xaxzpuSQO6U7PBy4jsn3LOo2hqhofvrUiuK7tGCRJZOS4TsTE2oOuxSdGMO68rnWa18DAoHHoM7A1cfFh5GSXBYid+3wqC37fSX6uA2eFh7ycckRJILVjHGed24VWbUI3I64LlphILDGRNd6naRple7MRTRLhx1Euq7E55Yq4a4PP6Wb5La+y7+uFCKKAYJLp/ciVdLvz4jobty3//Zb1j36Mr8LfFfxwPO6cRa/W2FNp/54CFs7bhaPMQ7uOcXz/+Xq8Hn1XpMksERVt5ea7hhmuRgODk5QKh4eZX25k8R+7cbmC622PxWQSufHOofWqgwtF0ZZ9rH9yBnnLt2FLiqHHPVNImTQcgKw/17Pk2um4CkpB1Yhol8TwLx4ipmdqo83fUIx+bo2A1+HEXVCKPSm2XsWInpJyvkqaFHACPEzisB6c89erIZ/95Yet/PjlRrw+FU3193oKdWqTZIH7nzqTDp0T/tHZTwYGJ4LM9GKWL9qPx+Wlz8DWdO6e2ODP2WN3/8L+PbVrs2Wzm3jzk0nI1TQkrS35a3YwZ+Rd+JxuqMy2lsOstL/qTDylFez7akGQ2LIpws6k/V8cNzX/mjAUShoBU5hNtyWEpmnkrdxO5u+rMYXbaHvJCMJbB3fEzlm6FdEs6xq3nKVb0DRN90NSkOfghy82BhR8hjJsggDJbWPo2OXv6z4wMDhZ+em7Lcz6ZhOKT0XVNBbO2023Xkncfu8ZNbacqY70A8W1v1mDvbsK6Ng1+Dumrqy88218DlfAaz6Hi7R3fvK3KdA563jLKvht9H84d/XbiNLfR83olCsFaCiqorDg4sf5/cx72PDUp6x9+ENmdrqanR/OCbrXFGYNqSIimUPvK9atTA/ZD0MQqFIlsFhlwiMt3Hz30Dq/DwMDg+rJSi9h1jeb8HoUVFWrlLbzsXVDNssX7W/Q2GFhtU/o0NBq3WBYC1FLB5C9cAO5S7dUN1FICjfvZeur39dqDScLhnGrI7s//p3MuWv8ux9VQ3V7UVweVtz2OuXpuQH3JgzpHlRfAv6WEylTRoV0bWiaFlL1JDY+jKtuHMj4C7tx1Y0D+e97F5KYVHOQ2MDAoG6sWLIPRafLvdvtY8HcnQ0ae+y5nWut6SrLEu06hG5GrLg9rLrnf3waOYGP5TP5oedUsv4IlP3LnLeGeeMfrP+CFZXtb/5Y/+dPAIZxqyNp//sp6FgPfoO0/9u/Al4TZYkxs57CFGGr6m8kh9uIaN+SgS/fHHKO3gNaoXd0k00ip5+RwtCRqVxyVV+GjkzFbDE8ywYGTYHHXXli08HrqT5ruSbGX9CNgUPaYDKJWKyy7slMEPxtcG75z7BqXaB/XvQ4aW/Nwlfur5Ur3rKfPyY+wqG/Nlbds+rOt4M6odQVT1F5g54/3hjfjHXEW65fbKl6fVV/XEeTcHo3Ljn4FXu/WkBFZj5xAzrR6pxB1fquE5pHcM6F3Zjz41Y8HgU0MFskYmLDOOeCbg1+Dz6fysol+1m5ZD8mk8QZo9vTs18LIxnFwOAo+g5szZ9zduJ2B2Y1mswSpw1r26CxRUnkhn8P4cLLerNvdwGRURYqKrws+XMPuTnlWK0ynbomMGJsR2LjQ6scFW/bT/aCDUFxfcXpZs2D7zNh6RtoqkrxtgMNWi+CQOLQhn/3HE8M41ZH2lwwlK2vfBdU6C3bLLQcN1D3GXNUOJ1vPLdO81x4aS+69WzOgt934Sh302dgK4aMTMXSwJOa16vw/CPzSN9XVPWh3bwui4FD2jD19tMNA2dgUEmHLvH07NuCTesycVcmdJnMEnHxYYw8q2OjzBEbHxZgvHr3b1XN3cHkr9mpq2sLULixUg1FEJDDbbqbb9Fiosf9l7Llha9RPV5/pqQoVGVSVj1vt9Dv2evrtLYTjWHc6kj3uyex57N5uPJKqgycHGal5biBxA3o1KhzdeqWSKdujZsFuXTBHg7uKwzIvnS7faxcup8hI1PwelVURaNT90RsRiNTg1MYQRC45Z4zWLl4Pwvn7sLt9nHasLaMGNvhpGnya28VT6j9qC0hGvC/j043jCftf7MDZQVFgfDkRPo8ehXtJg1n+zuzcRzIIfGMnihuLzvf/xVPUTmJQ7vR79nrT6pat9pg1LnVA1dBCVtf+Z4DMxdhCrfT+ebzSL3qzHqlyZbty+bgj0vRNI3kiUOITG3RBCs+wjMP/s7Obbm61yRZxGySQADFp3LFDQMYfqYhrGxgcLKiqSrfplyOIyMvIAlNtlvp/8I0utwyEfAnnfx50eNkL9jgP+kJYI2J5Kw/Xmry75zGxiji/huw8ZnP2PjM51WdBgRBoMe9k+nz+DVNNuezD/3Ojq36xu1YzBaJB54eS7sOcU22HgODk5nyMjdbN2YjSSLdeyedNCe2oyndncncs+/HmVOEIIoobg+dpk1g0Ku3BoUZirftp2DdLuwt42g+vBeC+PfLKTSM20lO3qo05oy6C6UiMINJtlsZ+/t0Eod0b5J5F8/fw6fvrQoKkofCYpUxmSS69Ejkost7k9QyqknWZWBwsjH35zS+mbHO3/UeAVVVOXNCZ/buzKewoILO3RKZcFF3EpqHVu7w+VQOZZZgs5uJjQ9DVTV+n72dOT9upazUTfOWkUy+qm9lhnT90TSN/DU7cOUWE9e/I7bEmAaNdzJjGLfjiOpTODh7GTmLN2FPiiX1ijHYW1R/2lk67WV/4fexqcaCQOoVYzjjk/ubZK0+n8rLT85nz858vzq5AKIghEx5PmpZWKwyj790jmHgDP7x7E7LY/pj86oVKhdFAbNF4pHpZ9MqOTro+uL5u/niwzWoioaiaLRuG01Sq2hWL9sfMK7ZLHHjnUPof3rj6Uf+k6mtcfv7nUlPMjwl5czqO43F10xn22szWff4J3zX4SrSf1lR7XPuwrJgwwagaf5rTYQsi9zz2GhuvGMIpw1ryxmjUhk3sUuNWZhapTrD959vaLK1GRicLMz9Ja3GWjZV1XA5fXzxQfAmfMuGLGa8t4oKhxeXy4fXq7B/TyFLF+wJMpgej8JXH69r1PUbNNC4CYIQIwjCPEEQdlX+V7c3gyAIiiAIGyr/N7shc55srHvkI0p3ZVal2aouD4rTzcIpT1V1AtAjeeIQ5DBr0OtymJU2FzStnJYoifQ7LZkzJ3SmrMzN2uXpaFrNEj+aBts35zTp2gwMTgaKCipC9kw8lrQth4Jem/X1piAjVp13JD/X4a9pNWg0Gnpyux+Yr2laB2B+5c96ODVN6135v/MaOOdJxZ7P/9BtbipIIpnz1uo84SflkuFEpLZAOqppoGQ1E94mkXaXjmqStR7NqqX7mf7oPNavziDnUBkej4KGhiSLCKIQ0tDZw06+gLqBQWPTvXcSJlPtvh711PpzsuvmfTGbJWTZcKQ1Jg39bU4EPqn89yfA+Q0c72+H6tFPzNA0rVq5G8liZvzS1+n54GVEdmhJRPsW9LhvChOWv4lsszTVcgFQFJWP31np31ketZnUVIiJtfO/LyZjtgR/YM0WiTHnNG4tn4HBycjocZ2w2k3UlEwoyyKDh6cEvd6idXAMDvyx62Pr0kxmiRFj29daHNmgdjTUuCVqmpYNUPnfUD0ZrIIgrBEEYYUgCP8oA9hy3ADddFrV7SVpVJ9qnzWF2ej98JVctGMGF+/8lD6PXY0pIriDdmOTnVES0An4aIoLK3BVeLnz4VFYbTJWm4zJLGE2S/Tu34ozx3du8vUZGJxowiMtPPnyeAYNTcFqlbHbTfTu3wqLRa7a+FmtMkmtIpl8dd+g5y+Y0jNIGFmUBGITwmneMhKLVcZqM2EyS/To3YJJVwWPEYrCTXtYfM10Zg+8hWU3v0rp7syGvdl/KDVmSwqC8AfQXOfSQ8AnmqZFH3VvkaZpQXE3QRBaaJqWJQhCO+BPYLSmaXt07psGTANITk7ud+BAA/XQjgNle7OYPeAWfA4XqueIe1KQJcLbNmfk148Q26duhdAVhwpZfc//OPDDElA1Wk84jQEv3dRoLd9zD5Xx0L9+0vXxy7LI6x9fTFi4Bbfbx4bVGeRkl9GydRS9B7RCOkrqx+NRcFZ4yDhQTFFBBcntYkhuqxt2NTA44eTnljPvlzQO7iuiTUozxozvTFxCeJ3GcJS7WbF4PyVFTtp1jKNnnxYhRY3XrDjIp++uwuHwoKkanbsncsO/hxAVbWXf7gIK8hwkpzSr6uqhKgqFG/aAALG92+tumtN/Xs6CKU+hurxoqoogS0gWE2fNfYGE0/9e2o/15biUAgiCsAMYoWlatiAIScBCTdOq9VsJgvAx8LOmad9Vd9/fqRSgIruANQ++z54Z84Ja1Ziiwpi093PdLrbO3CJceSVEpLZAroy9ecud/NDtWiqyC9F8lcZHFLHGRjJq5uOsf2IGOYs2IVnNtL9qLP2evR5TeHBD1Zp48PbZZGaUBLglRVGgQ5cEHnxmLAB5OeW89eJfZBwoQZQEZFnk8qkD6DuoFR+/s5LVyw+g+PwDmEwigiDQoUs8dzw40uhWYHBSsSstlxcfn4/Pp6L4VGRZRJJF7nl8NB06N7wJaChUVaOooAKrzURYeOgebplz17DoymfxuTyg+RPLRnzxEEkjj3h/VEXhq6RJuPNLgp6P6pLMhVs/CnjNkZHHttdnkrdyO1GdWtP13xfRrFvbRntvJ4rjVQowG7i68t9XA7N0FtJMEARL5b/jgCHAtgbOe1JhT4oFVb+rtur1sefTeQGvuQtLmXv2/XzT5lJ+HnwbXyZcwKYXvkLTNHZ/Og93YdkRwwagqngqu+Fm/7EO1ePDW1rBjv/7hTkj76q2QWEobrt3OGFhZiTZv2ZJEgiPMHPjHUMAfz3cMw/8xv69RXi9Cm6XD0e5h4/fWcGT984JMGwAXq+Kx6Owc1seX30cOpGmOg5lljLv5zQWzttFWWnoTFMDg7qgaRrvvrIUt8tX1Z/N51Nxu3y89+pSmrLWVxQFYuPDqjVspbsz+fPCR3HlleArc+Ird+LKKeKP8x6m/OCR7OTirfuD1P8PU7YnG1feke7ehZv28EO369j2+g/kLN7Mro9+46dBt3Bw9rLGe3MnOQ01bs8DZwqCsAs4s/JnBEHoLwjC+5X3dAHWCIKwEVgAPK9p2j/KuAEUbzuga2SUCjfFaQcDXpt7zgNkL1iP6vZW/jG72PjUp+z6+DcO/bVRt1+c6vIEJa+obi8lO9LJ+qPuNTJmi4QoCQiVfeM0DVwuH4eySgHYsCYDp9OLdkz6ssejkJ1ZGmDYjsbrVVg8f0+NReFHo2kaM95dycN3/szXM9byxftruPP6mSxftK/O78vA4Fjycx2UFOm3qioqdFKQ56jVOIcyS/n4nRU8/cBvfPb+anIPNU496vY3f0TxBiemqV4fO979uepn0WzSr40F0DQE0xFvydJp/8VbVlEVKtEUFaXCzeJrX0D1nRolBw0ybpqmFWiaNlrTtA6V/y2sfH2NpmnXV/57maZpPTRN61X53w8aY+EnG7H9OiLIwRmGcpiVmF5H1LQLN+6heOv+IEPlc7jY8OSnhCcnIOikFofC53CSt3J7ndf7yf9W4ijzVCWWqKqGx63w9kuLURWVnKyykEWsNW10vV4FRan9aXLN8oMs+XMvXo+C16PidvvwehQ+eHM5hfm1++IxMAiFKApU9ydbmzZPWzdm88hdP/PXvN3s2p7Hn3N28PAdP7MrrXY6rdVRsuMgmjf4s6Z6fBRvP7IxjurUGltznZi2KBA3sDOWaH/80OtwUrBul+5cmk8Jee2fhlFY0Uh0v3sSkuWYGjBRQLJbSL18TNVLpbsyQvZfqsjMp+MN4xFlnXhViDRh2W7F1rxuOnI+n8qWDdm6pyufT2XvrgJato7CZNY3sjV9FyQmRWCqg4Ge90uartalz6vwyjMLWPD7Ttyu4FpCA4PaEBsfRlyIhp9xCWHVNgMF/8bvvdeWBnTmVhQNt8vH+68va7BbM35QF0RrsNtSslmIP61L1c+CIDDim0cxRdqR7P5yITnMiiU2kmEf3xdwXyg0TUM4RerpTo13eRyI6tCKsb+/QFSn1ogWE6JZJuG0rkxY9mZAwkdU5+SQboHwNolEdWjFsE/uQw6zYoq0+/+QbRa63HK+rqIJokDKJcMBfwB56bSX+arFJL5rfwWbXvgKxaNjFGr4MCqKSs++LYiItAbV3pjNElExtpA1OWaLxGVTB1Q7/rE4yvXjCJoGB/cV8eVHa7nvllkUF1bUaVwDg8PcdNdQbDZT1abLZJKw2kzcfNewGp/NyS7F6dDfXBXkOSgO4fKsLZ1uOs+/MT7aKAkCktVEx6lnB9wb17cjk/Z+Tr9nptJx2gQGvHQTk/Z8HtC2RrZbSRzWQzfb0hRuI7Z3+wat9++CkdLWiCQO7saF2z/GmVuEaJJ1MySbdU8hrn8n8lZsDygdkOwW+jxxDQApFw+n9TmDyP5zPZqqkTSyN6YIO5bYCDY9/2XVCVGQJMbMegpzVDgV2QXM6jsNT7GjKhllwxMzyJq7hrPmvRiwm5NNEh26xLNjWy7H+msEAVI7xiFKIg8/dxbvvrqUndtyEUSBsHAzV980iLbtYnj9+YWkH/AHsH1eFUkSSW7XjEuu7EPXnkl1+r317t+SQ1ml+Lz6rky3y++m/PyDNdx6zxl1GtvAAKBtaizT35nIX/N2c3BfIckpMYw4sz2R0f6Np6pq7NmZR2mxizbtYti7q4B1Kw9itZvo3isp5OlMA6QGFl/bm8dw9oKXWXr9yxRu3gsaxA/szNAP7sEaGyxSbomJpNu/L6p2zKH/dzc/nXYbvgo3SoUL0WpGlERGfP3I37LNTX0wugKcADylDpZe/xIHf1qOKEmIFpl+T0+l8801K5O58oo5tGgTpgg7SSN7I1YGkVfe9TZpb81CPSYwLYdZGfPTMySN6B3welZ6CU/eNwevV8HnVRFFf6r/tDuGMGBwoDq5o9yN2+UjOsYecGLLyS6jvMxNq+QoLNb6y3KVlrh46F8/4Sh3oyih/x5lWeSD7y6v9zwGBnpkZ5bw4uPzcZS5QfAnVkmigKJoCALIJhGzWQ72MAjQJiWGJ/87vt5z+5xuVt75FntmzENTNcxRYfR56ho6Tzu3YW8K//fM7hlzyVuVRlTHVnSceo4/s/tvjtHy5m+Ap9SBp6gce8s4RJ1klGNRfQreUgemqLCgrt/fd7mG0h3pwQ8J0OuRK+mr0wC1uLCCP37dwa60PBKTIhg7oTOt2jRdEXaFw8PyRfvIziwlOaUZg4a2repGUFxYwaxvNrNyyX4qHB5dz6kkCXzw3eXVxhQK8x1sWJOJIEDvAa1oFtP0ii8GxwdVUdm0PouD+4qIjQuj/+DkGrtZ1GbMu26YSVGRM8iLcTSH6+JUVcPrUTBbJGRZ4uHnz6JlCKmtmvBVuFgw6QmyF2wISPGX7BaGfXgvKZeMqNe4/3Rqa9wMt+QJxBwZhjmy+mA2+FvJr39iBtte/R7F40W2mel5/2V0v2dy1Re9pZm+0oJkMVe5R1VFIfO31ZTsSCeqczItz+rPxVdULxHWWBzcX8RzD81F8fmzIS1WmW9nrOfRF8YRnxhBdIydq28axJRr+nLb1d8GKaoLAnTv06Jaw/bzzC38+OVGNEBVNGa8u4qzzuvClGv6NfG7M2hqykvdPP3AbxQVVuB2+TBbZD7/YDX3Pz22Qao42zYfwun0VmvYwB//HXdeFyw2E5kHiklOacaw0amEhYfWgdU0jbK92YgmKUBd6NDiTSy/9TWKtx3QTe1XKtysffgDw7g1EMO4/Q1Yfc+77Hj3J3yVXbs9bi8bnvwUxeOj98NXAND19gtZuvnl4Bo5AVImj6R4+wHmjLobb5kD1asgWc1Y46IZv/jVGhurNhRN03jrhUVUOI7sTt0uHx6PwnuvLeOhZ8+qet1iNXH1TYP45J2VeH0qmqphMomYrTJXXB86UWX3jjxmfb0J7zFxuzk/bqPC4eG6W09v/DdmcNz45N2V5B4qryoxcbt8uIHXnl3AS+9eUKt0fj0O7i2qVasZDQ2TSWL8BbWTuMqav47F176Au7AUVI2I1BaM+PJhNEVl3tn3V32WQ1G2N7tW8xiE5tSILP6N8ZZVkPbO7KAPg6/CxeYXj2RDpkwZSeoVY5BsZiSrGTnMimSzcMaMB9j7xXx+6DEVV04RSoUHzavgK3PiOJjDwsufbdL1F+Y7WL8qg4K88qBrmqqxd2c+jvLA9zZ0ZCoPPz+OIcNT6NwtkQkXdef5NyeS0Dw4QecwC37bGbJr8uL5/iQCg78nPp/K2pXpurWTZaVuDu4rqte4q5cdYOaXG1GrifMeRpZE+p7WulbjFqcdZP7ER6jIyEOpcKO4PBRv3c+vZ9zB2kc+xOfUzw4+GluCodHaUIyT20lO2b5sRLOsL7ujaDgPFRKenIggCAx+50663XkxmXPXYgqzknz+EPJWbGPdIx/puj80RSVvxTZcecVY4+sXNwhFcZGTt15cxL5dBYiiEHSiqkJAt0NBm3Yx3PDvIbWer6w09E5YVWH5ov0kp9StHtDgxOLzKhzYV4im+WNjeoii4HcrVqIoKrvS8vC4fXTokoDNpp/o5Hb7+L/Xl+HVKZ4+FotFZsRZHWodW9v6329R3MGfV8XjJXfJlhpLcWS7lR73X1qruQxCYxi3kxx7yzgUnWaoAJqmYo0LTBWO6tiaqI5HdpibXvi62r5yoiThKa3AGh+Npmm4cosQzSbdMobaomkaLzw6j+zM0hpluOLiw4iM0qnfqyO9B7Rk07rMkN8b3hB99wxOLjIOFrNpbSYZB4tYuyIdEKq6xOtl0iqKSkp7fwbgzm25vP78QrxeBUEQUHwqk6/uyxidNk3bNmYjhnBlCoI/vpZ+sBi73cyIsR3o2lOvMYo+hZv2oukYY6XCXW2MXbJZQNPocvv5dL39glrPZ6CPYdxOcqyxUSRPHEz67OWBGVU2f1cA2V69YXAcJbyqhxxmJbxtIocWbWLp9S9Rnp4LmkbcgM50mH4rvyzIJG1LDlabzKhxHRl/QTfdzsNHsystj/w8R7WGTRQFZJPItbeeXu94ydEMGZnKzC826p7gzBaJfqclN2j8slIXv83axrpVGdhsJkaf3YnTh6cYDSYbCU3T+OR/K1m6YC8+n1orbVKzRWLyVX2xWGTKS9289OR83K7ATczXM9bRMjmaLj0CjZOqaiFzSGSTxJRra0zGC0lMr1QK1u4MMnCy3ULy+UPYPWMuyjFhBnN0OKNmPkFsn/aYo+rWhsdAH8O4/Q0Y9uG9/HXlc2T+uhLRakZxeWh70RkMevXWGp+NP60rjgO5uqLOokVm0Ou3Ubork3nnBAa5D2zK4PvnlqBIfreOy+nlp++2sDstj7sfHV3tnNUJyoqiQMvkaNq2i+GcC7rRonVwkWp9sFhknnp1Ag//+2fKy468D7NZpEefFnTuXv9eeKXFTh6+8xcc5e6qQvOMA8Vs3ZjNtDtq7zr9J7JmxUF+nbmVwoIKUjvGcf7knrSuR/bi2hXpLFu4r9rkDpNJpHnLSJwOL3GJ4Uy4qDs9+viVOZYv3hck8g3gcSvM+XFbkHHr0qO5rqtTEPyiAsfic3nIX5WGZDERN6BTtYXQ3e+axN7P/wiMkwsCosVE/+euJ7pLG9Y+8H8IJhlNUbHGRzFm1tM06x7c0dug/hjG7W+AbLcy+vsnqMguoGzfISLbt6h1wLn3Q5eTPntZUBalZLcw5senaDGmH4uveyHI9bm3Yy8UIfCE5vUopG3NYd/ugipXkB4tW0eHTK1u0TqKp1+dUKu115VmMXb++/6FLPpjNysW7Uc2iYwY24FBQ9o06HT4y8ytOMrcAbFBt9vH6uUHGDexyykXy3OUeyjId7BqyX5+/2l7VSLPmuUHWbP8IM1i7Zx7cXdGjetY69/7/Dk7dPVFj8brVenYNZGrpg0MulaQ6whpGPN1kpnsYWYum9qfLz5cg9ejoGlgMktYrTKXHnNq2/3pXJbf+jqC6HeRmuxWRn7/BImD9TMnozq1ZszPz7Lkuhdx5hShqSrRXZIZ/tmDmKPC6Xr7BXS4bhwFa3dhirQT0yu1UbwXBoEYxu1vhD0pts4KA9Fd2zLuz5dZ8a83yF+1A9Esk3LJCAa9emtVXK1w/e4gF0pxbCLo7E5VVWPX9txqjVvb1Bhatonm4N7CAINgtkhcfHnvkM81BoIgYLebadcxloTmEfTsG7pTcm1ZuzJdN+lF8alsXp99Uhu3nOxSf9FzfBgp7WODvkQ1TWPvrgKKCipo064Z8YmhY60+r8KM91axbOFeJFnE5dQ3RkUFFXz18VoyDhRz9U2DQo5XkOfgx682snFdVlDGrB4Wq0xKqv7fXWqnOKxWGdcxbklREujYRb8Z6cizOtI2NZZ5P6dRkO+gW68kRp3VkfDII7VruSu2sezmVwPciL4yJ3PH3cekvZ8HxbwPkzSiNxfv+QzHwVxEkxRUbmMKs9H8jJ4AlB/IYesr35G7YhuRHVrS/a5JxPbpUOPvw6B6DON2ChA/oDPnLn8LVVEQRDHoCy6qS7Jf0+4ot47J48ZrDVb3kCQx4MOvhyAI3PPYaD54azkbVmUgiAI2m4kp1/Slz8DapVPXh8KCCp68dw4VDo+/0Ncs8d1nG7j/qTOrNcY1EaqruCiJmC21735wPPF4FN5+aRFbNmQjSSKaphGXEMY9j4+pUm0pyHPw4uN/UFhQgSgI+HwqfQa24sbD/HIhAAAgAElEQVQ7hyLrKMd/9v5qlv+1D69XDZ39enh+t8Li+bs5d1IPYmKD/44K8x08cufPOCu8tYqviaKAzW5i0NBAabiyUhc/fLmRlUv243b7EITAZESzWeKcamrTUtrHVuta3vLytyg6qfuaorD703l0v/PikM8KgkB4m+rd4QXrdzFnxJ34XF40r4+CNTs5MHMJwz65j5SLh1f7rEH1GHVupxCiJOm6P3rcMxnpmJYbrfZuQ1SCd+aCAP0G1Wyg7GFmbr93OG99egkvvHM+r310MUNGptb4XEP46K3llBQ5q5IKPB4Fl9PLG88vbFBbklFndQhpxI7V4TxZ+PqTtWxZn4238nfgdvnIzijl1WcWAP4T28tPzScnuwy3y4fT6cXrVVi/OoMfvtwYNJ7L6WXJgr21Kng+jCSL7NmRp3vtp++24HLWzrBJkkDXns15dPrZARsNp9PLY3f/ysJ5uykv80u2HR5NEKBz90Qeem5ctfWRNVG2J0s3dV9xeijf1/BC66U3/hdvmROtUhNWU1UUp5ulN7ys39HDoNYYJzcDYvt0YMQXD7Pk+pdQXB40VaWT1UnswBas2eyvUxME/070rodH1Ukk2WozYQ1Ra9SYeNw+tm7U71FXXu7h4L4i2rSrn/tw5LiObFqXRdqWHNxuH7LJf/q95qZBRDez1TyADuWlbnIOlRIbH17vMUKhKiqL/tgdVMOlqhpZ6SVkZZTg9Sjk5wRntHo9CvPn7GDSlYGybMVFznplhoZH6J/yN63LDCmSHRZuJiraxvAz2zNkZDssFln39Lzkzz2UlbpQjnYZa4fbLvVn5NiOdV7vsSQM7krR1n26zUR3fzIXyWqm96NXBbS1qi1eh5PCDXv0L2oaBWt3knB67RRRDIIxjJsBAMnnDWZK9rcUbzuAbLMQkerXcczLKSNtSy72MBM9+rbEHKKB6YlGqSa12+3y8cqzC+jdryWb1mVRXFhBfPMILr68d61OXpIkcufDI9m1PY/NG7Kw280MGtqGmDh/zZLT6eXbGetYunAfXq9Cp64JXHZdf92sQZ9P5eN3VrB80T5MJgmfV6FH35bcdOeQBnVWOJrDnR700DQoLqrA61ERJX1j5azwoipqQKyyWaxdNxuxOiwWmU5d9eNdYWFm8gnusm62SFx0eW9Gn92pxvE3rcvSVaXxuBW2rM9qFOPW/e5L2D1jHj5vcM82b1kF29/4gew/1zNh5VtBYuY1IVRzv6ZqiOam3xT+kzHckgZViJJETI92RLZvWeW+jE+MYNjoVPqdllwrw6ZpGiW7MijZldHgDsV1wWYz0So5tIJEUX4FC37fRUGeA0XROJRZynuvLWXx/N04yt3M+XErrz23gK8+WkNOdnApgyAIdOyawEWX9ebs87tWGTZV1Xjuobn89cduXE4vik9l26ZDPHX/b+RklwaN8/Una1mxeB8+r4qzwovXq7JpbQbvvbas0X4XZotMM504F/gN39rl6bRJaRZSnaN5i4igJByLRWbU2R1rjDFKsoDVZiIi0sJ/Hh8TMplnzITO+mNpMHBI7Vy9UVFW3a7woghR0Y1zGo5ISeKcRa8GdMQ+GsXtpWRnBplzVule1zQNVdH/PctWM0mj+iDo/I5MEXZi+5waTUWbCqPljUGjkbN0C39d8SyuPH8TU1tCM8747MGQKdPHUrh5L2lvz8aRnkvSqD50nHp2nQpa9+zMZ/oj8/B6lVrFcsDvAhMEAY/bL+QsSQKSLHLbPcPppVPvdCyb12fxxvS/goqHRRGGjEjl+n8NrnrN61W4+bKvdJMxBAFat2lGdmYp4ZEWxp3XhbHndqm1K1BVNTaszmDpwr0AxCeGM+fHbbr3mkwir310Md/MWM+yv/YGnH7MZolb7zmD3gNaBc+hqHz7+QZ+nblVd1yzRWLIiHb06teSHn1b6ialHL3eD95cxqolB/wKJJKIpmrc8p9htU462rsrn+cemhsUBzSbJR6ZPq7Rs1h/6DGV4q37da91/88lDHjhxqqffRUuVt/3Hrs++g3F6SGmZzsGvXorzYf3Cniu/GAOP592G96yCnwOF5LNjCBJjP1teq0/N6caRj83g+NK+cEcfuh2XVA9nRxu44KtHxLeWt89dZjdn81j2Y2voHq8aIqKZLdgjgzj3NVvE9YyvtbrOJRVytefrGP9qvSaJPwAqnb+x95rDzPxxieXVPsFDTDr603M1EnAAEhoHs6L/zsio1RcWMFdN8ystiHrYQ4bimtuPq3Ge1VV443pf7F1Y3aVkbVY5JB1Yza7iTseHEnHLvH8+sNWfpu9nfJyDy1aRjL56n41GvUfvtrIL99vDTr5WW0yb3w8KWR2qR6Z6cVs23gIq81E30GtCQs31/zQUcz9aTvfzFhf5WJVFY3LpvZn1Li6uyRLd2ey8ZnPyVm8CVvzGLr/5xLanD+06vrvY+8l6//bO+/wqKr0j3/OvdPSEyAQQuiEEnpQEFABKdJBmrg2VETXxfITV9e2ir2sWFEsK7gWVDoC0pEqTUAIhEAoAUJLIJCQkEw7vz8miQkzk0wKIYTzeZ48ydxy7jvnubnfe855y/I/3M7TLSbav3ofrSeMAlyjtV97PEnqpvhC8aO6v5l+K94jvFPhUaAt8yIHf1hJ6pa9BEXXIfreW1Xi5CJQ9dwUFcrez+a7VQEHcFrtJEz5hQ6vP+D1XFtGFhseer9QDkxHVg7ZOTa2PPU53ae/4LMdEZHB3D2uI7u2HfcpKa43AZROOJCQQrOWRbtyh4T5YTLrHtd+Qi8plBoYbPF5RGnNcbBu5UGG3t7GrZ1L2bH1WCFhA4oMiHbYnYSEWdB0jYEjWjNwRGufbMpj0PBWHEhIZd+eU9gdMv8F4InnepRI2MAV8F/aYp8AfQa14IabG7Lzj2QQ0K5DVLGhKp5IizvEwq6PYs/KQTqcZBw8wZq736TlkyOJnTgGgJjHh3H6990eykoJGt/ZK/9j6pYEzmzd55YYIa9OW99l/ym03RjgR7MHB9DswdJX9Fa4o8RN4TNSSlK3JpC+P5mQ5nWpEfvX23Fa3CGcHpITO6020nYdLLLdE6t2oBl1HJes2UuHkyPzS74WVa26P02a1WBf/OkiR0m6QeCwe94vKbZ+JeBaH5o+1X2GwWw20G9oTKFtBoNGQKCJCxnFlzwBMBg1DiWeoX3HosXt99WH3KZF87g07kvTBJF1Q6hdp/RpzwxGnade6smBfans23OaoGAzHTrX85qB/3ITHGLhxlvKFmayecJn2DIK34D2zGzi3v2JmPFDsYSHUnfADbR8ciRx7/6EMLjCaqTTSffpL+Af8dcU6Jk/9nldbz6zLbFMdip8R4mbwieyU8+z9NZnOL/vKGgCnJKwVg3p/etbmEMDqR4bzfHl23Be8raqWUxUj634bAuP/PNm3n15OadOZCCEa7Si5QYza5rA6ZA0bhYOUrJ39ym3EZwQ0KRp8UVc/QNMTHixJx+8sSpfSB12JwOGtSS2wNpRRno2B/edoXW7SDatT/JpBOd0SoJDfaiYUMSyXHCIhawsG7oukBKqhwfw+HM9im/TBxo3rUFjH/qovJBS4rTa0M0lm7r0hZNrdnrcrhkNnFyzkwbDbwYgduIYmj88iOPLt2HwM1GnX0eMAYWdV/yjaiC8TGf716682WyqGkrcFD6x+s43OLvrINL+1/Tbme37WXff2/Sc8yrNHhrErrd/dDtP0zWaPTSoyLZr39K+ULt5CINO/dtKl5g4OMTCK5MGcPjAWU6fzCAyKoSo+qEcPnCWlFMXiKwbQlS9UE6dSGfiP3/FanVgszryqxWMe6JrsdUP8mgaU5OPpo1kb9xJsrPtNIupSVCwS5SklMz8bjuL58djNOo4nU4krsDkPDHUdeHKUl9A74Rwefw1ii5ePLp2a8SOLcc8To3WjAjk/vFdOHo4jRo1A2gUXaNC8xjas61se/Fr9n21CHtmNjWub0anSY+4rTsVhdPuYNtLU9n7yVxsmdkE1AnnurcfpNHoW8rNTt1icnsxA0DgFsPmX7s6Te7u7bWtqL4dMfpbsF/ILjRsNvhbaP306HKzWVE0yqFEUSwXT6fxU51RHmtUaSYjo0/O5NiiTazPDQIviLlGMKOPz0QzFC0UB6avYP3Y93Da7Ei7A93fgjk0gEFbPitxPs2SkpGezaol+9kXf5patYPo1b9ZmabtCrJ+1UGmTdnoJjxGk0aN8ED8Akz06NOEbZuP5abKcglPQKCZZ17pTa3axWfXcDolT/99Limn3BMEG00aL7zZlwZecjJebhb3eorTG3YXui8M/mb6r/3Q5/yJa8e8zaEZqwutyep+ZiK6teX0+jjsWbmi+f4/SiSaBdn4+CckfLHATeBMYUHccXImmrFk44Bz8UksG/As2anpCE3gtNpp+X8jiH3tfpUkuYwohxJFuXF49lqPwgaAlFjTMtj51g8eq4U7c+wkL95M3YGdi7xG4zt6Uq1tY7a/NI0zf+zHL7I6bZ+9A7+Iyz+NExRsYfDIkjlV5HExy8qu7SdwOp20ahvp5sywcHacxxGVrmuMuieW2NxUZjf3iuZE8nkOJ54ltJofzVrW8jkMQNMETg8ljcDlPbj7z5NXRNxStuwlZWO8231hv2hl24tT6b3gjWLbyDqeyqGff3Nrw3Exh+TFf8WWpWyMZ3HPCSUSzYJ0eP0BTv++m/N7j2LPzMbgbwZN0GveqyUWNoDQFvUZceB7UrckkHM2nRrXN8NSvXxemBS+ocRNUSSJ3y1j46Mfe93vdDgJqFuTrOQznvfb7WQe9ZxfsCBSSvZ8OJvkxVuwZ+Vw4fBJfhv9GnUHd6Hbd89VyrfdDasP8vXkjei6BkhX5ecxHehdoPLzuTT3zBYADock7UxWoW2165Te0cPP3wRkuW3XDRp+/lfG0SN1816PdQSRkpRN8T61kbbrEJrZ6PHF6VLsWTls+/c0ev/yeklNxRjox6CNkzmxcjunN8bjHxFGg5HdylQ4VAhBeEf3KuCKikFlKFF4Jc9FH2+jNsASHoJm0L1mUxCaRjUfMi2cWLWDgz+scLlZ506V2zOzOTp/A0cX/F66L3AZOZmcztTJG/MTE2dftGOzOfn5f9s4sC81/zhvIyZNQIMyVCq4lJ79mnp0w5cSrutctirkpcWvdnWvox6/Wr7FcQXUDfcYYuKN5KVbWHvfO6wcOZEDP6woUfJhoWlE9upAuxfuounYAaoi9lWOEjeFV46v2IZWhFOFZjQQ86grSDn21fvQ/QtPyWlmI9XaNvZpHeTAt8vc44dwCVziN0uLPDf1j31sfmoKv4//iBO/7aiQtF+/LduP3YPo26wOli/cm/95xF3t3NJMGY06DRpXp1G0d3FL232Y1Xe9zqwWY1g28DlOrdtVpD3de0fTNjYSk1lH1wUmk47RpDPuia4Eh/jgcXkZqDugE5rJXdwM/hZa5gY8F0doTAPCYuojilmzzUPaHCR+s4SkWWvY8NAkFnZ9DPtF77Xi7NlW9k9dzMqRE/n9Hx9wZvt+n66jqPyoaUmFd4rRiOBmUcQ8NgyAmp1b0mvea2x6/BPO7T2CbjLS+K5edJz0iE9TikW9nRe1b/M/p7Dno9n5Wdv3fjqPsLaNGbz1sxInsi0JaWezcHqIoZPStS+PRtE1eOrfPfn+v1tJOnQWs8nATb0aM+qeWK/9cmp9HEtvfRpHjitbS3rCUU78toMunz1Bk7v7eDxH0zXGP9ONQ4ln2LPzBBY/I9d3qX/FhA1AN5vou/w/LO33L9eLiwBnjo1mDw8kesytPrfT65fXWT74BdJ2H0YzGnDmWHE6nB4z9RfEnpnNuT1JxE+eS+unbnfbbz1/gQWdHyXz6GnsmdkIXWP/tKV0eGssLR8dVuLvq6hcKG9JhVdsGVlMjxhRyEsNAAE1OrWg38pJGCzuMUcOqw3NoCM8VPL2xpH5G1h91+su9+kCGAIsdP1ygke375RN8Sy8+XGPD7l6t91Iz1kTfb5+SVmzIpHvvtziFjxtMukMHd2GAcNauZ3jdEqfnETmtL6fc7uT3LYbg/254/Rs9ALZ4tMTk0lesgXdz0y9IV0ui9OCPdtK6qZ4hNFAeKfmJX5pcDocnF4XR87ZDMI7x+AfUQ0pJYdnriFu0gyyT6dRu3s72j5/J0GNIr22cy4+icxjKYS1bsSJldtZc/cbPkXah7VqyNCdX7lt3/LMF+z5aLabh6RuMTHi4PeFArMVlQflLakoM8Ygf7pMeYIND39QKOejOTSQnrMmehQ2oNDD11fqDryBGh2acmr97vyYN81koPp1TWngpSJx4rdLvb69H523AduFi17rbNmzrSQv2YI9I4uI7u0IiPI9fyXADTc24JcZuziTmpVfTyyvWnT3Pp699XwRNuv5C5xPOOZ1/9ntiYR3aoGUkk1PTGbflwsBELrGxvEfcePUp2l0e/kEaQMc+GE5G/7+gWuUKUG3GOnx80tuCYCLQtN1t+O3PvMFez+bnz8VnXhkKYdnrWHgxsmENve8Rhjaoj6hLVwVA6L6d0I3m3xyNPH2An/whxUeY9uErnH0l99VOqyrHCVuiiJpcncfqrePZu9n87hwNIXInrFE39cXU3BAuV4n4+AJUrcnXhJyIAisV9NrjFxOmntpmgKnkp6YTPV27s4sJ1ZtZ8Vt/wZcdbOkzU7zfwzh+ncfLnYK9eKps2QmpxISHcVL7/Rn5vfb2bQuCemUxHaqy8h72hMQWPLchnloJqMrrZOHfdLhxBDgmmZMmrOO/V//6vZwX3f/O9Tq2qrEYu2J1D/2sX7cJBxZf43cbRmwbOBzjEj8Fr9apRvZZCanEP/J3EK2S4cTW3oWm5+aQh8fQgTMoYFE39+X/dOWFLLvUnQ/E429BFx7nbWSeE86qrhqUA4limIJa9WQzpOfoPf812n5+PByFzaAbS9OxXFJRgen1cbhGWs4n3DU4zmNRhUxQhHCY4xczrkLLB/yIrb0LGzpWdgvXMSRYyPh8wUcnrHaa3PW9EyWD36eGQ3vZPEtE5heazh7XpvGvQ915NPvRvH2y10Z3r0WIaVI2lsQg5+ZyN6xHh0o/GqFEdqyAQDxH8/x6IAjnZIDP6wokw15xE2a4XFkJB1O9k9bUup2T/72p1dHpeRfN5F18qxP7XT6cDwxjw3DEOiHZja6foyG/FIPhgALIU3rEjN+qMfzG93ew2NBUOl0Undg8dUYFJUbNXJTVAqOL//Dc0yUJjixagchzdxrfNUd1Bn/qBpkHUstvENAZK9Yj2smh2es9vhWbs/MJm7SDBqO6u7RvlUjX+Hkmj9x5tjyH/gJn87HkW3l+NKtZB5LQdM1hMFAlyn/R8ORnqdSfaHrl0+xoMuj5JxJx37hIoYAC5rRwC2zJuaPLHPOuBdCBZfDhrd9JSXjwHHwkAPTkW117SslhkA/70tlEjZP+JTu3xdfCULTda57YyyxE8dgTc/EFBrI6XVxJHy1ENv5LOoPv4lGo3t4zUXZ9vk7OTJvPVknz7pGf0Kg+5lo/+978I+suJyZisuDEjdFpcAY5O/xoazpGqYQzyNFoWnctmcqv3Z/krM7EhFCIDSN8M4xXsvkZKecw37R8zpN9ulzHrdnHDrBqbU73dZn7FnZ7J08N9+pIW/1b+2YtwmsX6vUAbz+taszPOEbjsxdz9mdBwlqGEHD23sUWj+MGngD5/cfc7PJEOhHZM/YUl33Ump1bcXZHYlu1R4MgRbCb4jxclbx1Ln1eu8Zb4Ajc9aXqD3NaMh3pIno1tbn9UBztWCG/Pklid8s4eiCTfjVDKXZw4OoWYbvpqg8lGlaUggxUgixWwjhFEJ49V4RQvQVQiQIIRKFEP8qyzUVVZPmjwx2i5MD1yCr7mDvqbtMgf4M2TqFEYnf0euX1xka91/6//a+1wDcml1aYvB3f5MXukbtHu08npNx6CSa2YuTjIchiCPbyq533JNIlwTdZKThqO50eO1+mj7QP1/YHFYbqX/sI6p/J4zB/oWmL3U/E9XaNSayV/mIW8wTw9EvcRoSuoYpJJBGo0vvtGKwmGj7/J1e93scwV8mjAF+tHhkKH0WvclN055RwlaFKOuaWxwwDFjj7QAhhA5MBvoBMcAdQgh1BykK0fLx4UT2jEX3N6OZDBgCLRgCLPScPdGtpIgnghpEENW3IyHRUUUeF9GtLdXaNSn80BYCQ4DF6wM3pFkUTh+88vKR0us64aXYs63seu9n5rS6n1nN7mX7y9Owpmd6PPbAD8uZXms4i2+ZwNK+z2AM8KP+0Bvxi6hGYIMI2j5/F32XvVuiEIyiCKxbk/5rP6TWja0QmoYw6EQNuIGBGz/B4F+2+LmYR4eh+7m/zAhNo07fjmVqW6GAcopzE0L8BjwlpXQLTBNCdAZellLemvv5WQAp5ZtFtani3K5NUrcmcHLNTszVgqg/7KbL4rxiv5jDn6995yrDcjGH2re057q3HvTqgg6w6vZXOLrgdxwFpjQ1k8HlWHfJtJ3QNRr9rSc3f1P0JIXT7mBRtyc4u+NAfiyhbjERUDecwds+LyTqpzbsZkmff7p5BprCghiVNN1ryENxnN15gOPLt2EK9qf+sJswVwv2bKvNDkIUW92hJOyftpjfx3+EM9uGdDrRLSYMARYGbf6UoIa1y+06iqqFr3FuFSFuI4C+UsqxuZ/vBjpJKcd7OHYcMA6gXr16HZKS3ANZFYorgSPHysbHPuHAt8tcjgcmA62f+xu7359FTsq5QmtIhgALgzZNJjSmQZFtJs1Zx5p738J+oXByZd3fzHVvPUjM+Nvyty0f/AJHF250c4YxBFjo9OF4mt7fr0TfRzqdrLnnTZLmrkfaHWhGA9Ip6fb9c9QfemOJ2gI4syORXe/8yLndhwlr25g2T48mrFXDYs9L3baP+I/ncCHpFLV7tKf5w4OwhIeW+PqKa4dyC+IWQiwHIjzsel5KOc8XWzxs86ioUsovgC/ANXLzoW2FotTkpGVwfPk2hCao0+c6jEH+Xo/VzSa6fv4knT74B9a0DCw1w9AMOo1u78Ha+97h9Po4QBDUMIIunz9ZrLABHFmwwU3YABxZOSTNWltI3NL3H/Pq5Zl+INmn71uQfV8vJmnu+vyRYJ7TyOo732BU0nQsNXzPdHJ00SZWjZro8iJ1Ss7tTiJp9lp6zX2VyF4dijy3RmxTbpr6TIntVyiKo1hxk1L2KuM1jgEF/bijgNL7ESsU5cDeKfPZ/ORnufFWAqfdQdevJtD4jp5FnmfwM2MosFYUWK8W/Va8hzU9E2eOrUSjDlNIIELXPHoOmkILO8RU7xBN+v5kN2cLQ5Af1do09vmaecR/Msdz8LOAwzPX0Pzhoqun5yGdTleR2gJtSacTR1YO6x58j5EHv6+U5YoUVZ+KCOLeAkQLIRoKIUzAaGB+BVxXofBIypa9bJ4wBUe2FVvGRWwZWTgu5rB+7Huc3+fdEUQ6nSR8sYC5bcbyU73RrHvgXS4knQLAFBxQ4um06DG3egwiNgRYaPbQwELb2jx7p0fPRXNoIPVvK/k0os2L04rTasd63r2it5QSW0YWTnvhdGfpicnYMtzryIErtCLzyOkS26ZQlAdlDQW4TQhxDOgMLBRCLMndHimEWAQgpbQD44ElQDzws5Ryd9nMVihKz97J83DkeKgabreTkJur0RNrxrzNpic/JS3uEFnHUkj831LmtR9HxqETpbKjWpvGtJ94L7rFhGY2Iow6up+Jpg/0p86t1xc6NqxlA3ovepOQ5vXQTAY0o4HaPWMZsOHjUuXyjOrX0WMWFN1spPYt7QttS5q3npmN7+L76kP5Lngg6x78D7ZM13Sq7mf2GrPmchK5MoVSFYoyBXFLKecAczxsPw70L/B5EbCoLNdSKMqLzOQUj5k3pM1B1jHPVcPP7TlM0qy1hSokSIcTW0YW21/+pljPSG+0fup2GozoRtLstUi7g7qDOucnB76UiJvbMGzPVHLOpqOZjKX2kARo89ydHPp5NdbzmfmJqnV/M5G9Ygm//q/g8+SlW1n9t9fzv7fD7uDg9yvIOHCcfisnEVi3JsHRUaTFHSq8JqgJqrVtXOr8kwpFWVG5JRXXHJG9OqD7uQdyGwIsXh0gjq/c4dGhQzqcJC8pW8hKUIMIWj05ktZPj/YqbAUxVwsuk7ABBNQJZ8j2L4i+vx/+UeGEtKjP9W+Po8eMlwsd98fzX7mVPHJkW0nZvJczOxIB6P7jC5irBeUndTYEWDBXD6bbd8+VyUaFoiyo9FuKa45m4way58PZZNvO/1Vex2jAEh5Kozvc68YBmEICvFaDNgZ797KszAREhdN1yv8Vecy5+CMetwtNkLbzINXbNSG0RX1GHvqBQz+uJC3+CNVaNaTBqG4+Bd8rFJcLNXJTXHOYw4IYtOVTGo7ugSHID2NIAE3u7cOgzZ96zbxRb0gXj1OZur+Z5n8ffLlNvmL4167ueYcQBNavlf/RGOhH07ED6PTe34m+r68SNsUVR43cFNckAXXC6fa/Z30+3hQcQI+ZL7FqxMsgBE6rHc2oU7tXB2Ieva3Y869W2jz7NzY99gn2rL/K6whdw69mGLVuan0FLVMoikaJm0LhI1F9OzLq2M8kzVqD9dwFIrq3o0aHplfarMtK9H19uZB0krj/zEAzGXDa7IQ0jaLn3FfLLYelQnE5KJf0W5cDlVtSoag8WM9f4OzOg1jCQ4vMwalQXG7KLf2WQqFQmEICibipzZU2Q6HwGTWvoFAoFIoqhxI3hUKhUFQ5lLgpFAqFosqhxE2hUCgUVQ4lbgqFQqGocihxUygUCkWVo9LGuQkhUoCkK22HF2oAqVfaiKsY1X+lR/Vd2VD9VzYqQ//Vl1KGF3dQpRW3yowQYqsvQYQKz6j+Kz2q78qG6r+ycTX1n5qWVCgUCkWVQ4mbQqFQKKocStxKxxdX2lYrGBoAAAONSURBVICrHNV/pUf1XdlQ/Vc2rpr+U2tuCoVCoahyqJGbQqFQKKocStx8QAgxUgixWwjhFEJ49RQSQvQVQiQIIRKFEP+qSBsrM0KIakKIZUKI/bm/w7wc5xBC7Mj9mV/RdlYmiruXhBBmIcRPufs3CSEaVLyVlRcf+m+MECKlwP029krYWRkRQnwthDgthIjzsl8IIT7K7dudQojYirbRF5S4+UYcMAxY4+0AIYQOTAb6ATHAHUKImIoxr9LzL2CFlDIaWJH72RMXpZTtcn8GV5x5lQsf76UHgDQpZRPgfeDtirWy8lKC/8WfCtxvX1WokZWbaUDfIvb3A6Jzf8YBn1WATSVGiZsPSCnjpZQJxRzWEUiUUh6UUlqBH4Ehl9+6q4IhwDe5f38DDL2CtlwN+HIvFezTmUBPIYSoQBsrM+p/sQxIKdcAZ4s4ZAjwP+liIxAqhKhdMdb5jhK38qMOcLTA52O52xRQS0p5AiD3d00vx1mEEFuFEBuFENeyAPpyL+UfI6W0A+eB6hViXeXH1//F4bnTajOFEHUrxrQqwVXxrFOVuHMRQiwHIjzsel5KOc+XJjxsu2ZcUYvqvxI0U09KeVwI0QhYKYTYJaU8UD4WXlX4ci9d0/dbMfjSN78A06WUOUKIh3GNgm+57JZVDa6Ke0+JWy5Syl5lbOIYUPDtLwo4XsY2rxqK6j8hxCkhRG0p5Ync6YvTXto4nvv7oBDiN6A9cC2Kmy/3Ut4xx4QQBiCEoqeSriWK7T8p5ZkCH79ErVmWhKviWaemJcuPLUC0EKKhEMIEjAauaY+/AswH7s39+17AbSQshAgTQphz/64BdAX2VJiFlQtf7qWCfToCWClV0GoexfbfJWtEg4H4CrTvamc+cE+u1+QNwPm8ZYfKhBq5+YAQ4jbgYyAcWCiE2CGlvFUIEQl8JaXsL6W0CyHGA0sAHfhaSrn7CppdmXgL+FkI8QBwBBgJkBtW8bCUcizQAvhcCOHE9dL1lpTymhQ3b/eSEOIVYKuUcj7wX+BbIUQirhHb6CtnceXCx/57TAgxGLDj6r8xV8zgSoYQYjrQHaghhDgGvAQYAaSUU4BFQH8gEcgC7rsylhaNylCiUCgUiiqHmpZUKBQKRZVDiZtCoVAoqhxK3BQKhUJR5VDiplAoFIoqhxI3hUKhUFQ5lLgpFAqFosqhxE2hUCgUVQ4lbgqFQqGocvw/gmZ9xehf49gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25343317f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
    "from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# load image dataset: blue/red dots in circles\n",
    "train_X, train_Y, test_X, test_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would like a classifier to separate the blue dots from the red dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Neural Network model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with:  \n",
    "- *Zeros initialization* --  setting `initialization = \"zeros\"` in the input argument.\n",
    "- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values.  \n",
    "- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n",
    "\n",
    "**Instructions**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (2, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
    "    learning_rate -- learning rate for gradient descent \n",
    "    num_iterations -- number of iterations to run gradient descent\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = [] # to keep track of the loss\n",
    "    m = X.shape[1] # number of examples\n",
    "    layers_dims = [X.shape[0], 10, 5, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        a3, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Loss\n",
    "        cost = compute_loss(a3, Y)\n",
    "\n",
    "        # Backward propagation.\n",
    "        grads = backward_propagation(X, Y, cache)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the loss\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Zero initialization\n",
    "\n",
    "There are two types of parameters to initialize in a neural network:\n",
    "- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n",
    "- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n",
    "\n",
    "**Exercise**: Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry\", but lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_zeros \n",
    "\n",
    "def initialize_parameters_zeros(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # number of layers in the network\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.zeros((layers_dims[l], layers_dims[l-1]))\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        ### END CODE HERE ###\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[0. 0.]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_zeros([3,2,1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using zeros initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6931471805599453\n",
      "Cost after iteration 1000: 0.6931471805599453\n",
      "Cost after iteration 2000: 0.6931471805599453\n",
      "Cost after iteration 3000: 0.6931471805599453\n",
      "Cost after iteration 4000: 0.6931471805599453\n",
      "Cost after iteration 5000: 0.6931471805599453\n",
      "Cost after iteration 6000: 0.6931471805599453\n",
      "Cost after iteration 7000: 0.6931471805599453\n",
      "Cost after iteration 8000: 0.6931471805599453\n",
      "Cost after iteration 9000: 0.6931471805599453\n",
      "Cost after iteration 10000: 0.6931471805599455\n",
      "Cost after iteration 11000: 0.6931471805599453\n",
      "Cost after iteration 12000: 0.6931471805599453\n",
      "Cost after iteration 13000: 0.6931471805599453\n",
      "Cost after iteration 14000: 0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25343933c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.5\n",
      "On the test set:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance is really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0]]\n",
      "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (\"predictions_train = \" + str(predictions_train))\n",
    "print (\"predictions_test = \" + str(predictions_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 300)\n",
      "(1, 300)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253439a75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "print(train_X.shape)\n",
    "print(train_Y.shape)\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.reshape(300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is predicting 0 for every example. \n",
    "\n",
    "In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n",
    "- It is however okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Random initialization\n",
    "\n",
    "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n",
    "\n",
    "**Exercise**: Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights  match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_random\n",
    "\n",
    "def initialize_parameters_random(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # This seed makes sure your \"random\" numbers will be the as ours\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # integer representing the number of layers\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * 10\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
      " [-18.63492703  -2.77388203  -3.54758979]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[-0.82741481 -6.27000677]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_random([3, 2, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 17.88628473   4.36509851   0.96497468]\n",
    " [-18.63492703  -2.77388203  -3.54758979]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.82741481 -6.27000677]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using random initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sunst\\Desktop\\代码作业\\第二课第一周编程作业\\assignment1\\init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "C:\\Users\\sunst\\Desktop\\代码作业\\第二课第一周编程作业\\assignment1\\init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: inf\n",
      "Cost after iteration 1000: 0.6250982793959966\n",
      "Cost after iteration 2000: 0.5981216596703697\n",
      "Cost after iteration 3000: 0.5638417572298645\n",
      "Cost after iteration 4000: 0.5501703049199763\n",
      "Cost after iteration 5000: 0.5444632909664456\n",
      "Cost after iteration 6000: 0.5374513807000807\n",
      "Cost after iteration 7000: 0.4764042074074983\n",
      "Cost after iteration 8000: 0.39781492295092263\n",
      "Cost after iteration 9000: 0.3934764028765484\n",
      "Cost after iteration 10000: 0.3920295461882659\n",
      "Cost after iteration 11000: 0.38924598135108\n",
      "Cost after iteration 12000: 0.3861547485712325\n",
      "Cost after iteration 13000: 0.384984728909703\n",
      "Cost after iteration 14000: 0.3827828308349524\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25344c0dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.83\n",
      "On the test set:\n",
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff; a more numerically sophisticated implementation would fix this. But this isn't worth worrying about for our purposes. \n",
    "\n",
    "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
      "  1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
      "  0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
      "  1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
      "  0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
      "  1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
      "  0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
      "  1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
      "  1 1 1 1 0 0 0 1 1 1 1 0]]\n",
      "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
      "  0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
      "  1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (predictions_train)\n",
    "print (predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aNCt/pZJifqa9kHFK4Yt7PRXBBbBcNmVuloqgX5xf60Nfv9XU3GvZNO0HCtJpv9xeGPO53izrlALmptv3UanQqWcr8H3F4rzXYG72PVhZ8s7IOR944yNnpF3N1nOuC8pmb6ymj6aIvFlEHhKRh1Z85wx3S3O+E4tL2Uxav94woae3sxmJ1ZVGD9bQdOuTy/rMnnBYmHMbwj5cVzUVXLCmzfqnkINAKVhdWTtnPGEwMmYjBhjG2mDgjo9dTWC1eAUo8FzF4R+XODHlMD/rMjUZzpEGgSK90l5IisDwmLWh+bZUDDh+tMQTjxZ48rECx4+W8JqYxDdLqRi0nAuunZfWaDphq+coN2IK2FGzPA6caLajUuojwEcALo/3XhweBJqTRkQY3xVlYdYlnQ4FXiplMjRqY3Q4h9VOUBw/6lS3Ly14jGyzqwI4GjVazhlWNLBY3KCQP4UX+rpL6OmzuPnnFMVXvZY3/R2s9vfzke/DHYNz9M/MNoyYU90mMyechnlcx1EszLkthqsh8bgwPBYhFm89DnedgOPHnDqNtiLEioUiuy+JnVK4jmlKy/tjnR5H36b88Vf+mpj2gL3gONcF5V3Ar4vIPxA68aSVUtNb3CfNBYJhhC/04ZOc9U6kDPLZ5sJsvclv9oRLV5eJYQqWLXT3mKym6zVSMaBvIHwkh0dtjh4uNbzsEynBdcB1WksqEbj0aYJ918ur66ov7n8C+tf2ve+O27n97z+N4fvYrodlh4OIgSGLQ0+WWE/gw3Q+wu0vdrj3c42DBREYG4+w0jXAQ6nduIbJ3twUO/LTVdld8Tj2WlioPR+mjpawTIOubpNU9+ZjMqMxg0hUGkzLItA/cK6/9jTnGlv6ixGRTwO3AIMiMgX8HmADKKU+DHwVeBFwAMgDv7Q1PdVoGhkZszl6qBSGRKjwJdxKixGBfD6oBvmPbLOJxISVRR8/CD0yh4YtLCsUCLG4wa49URbnPYrFgGhUGBiycZyAmeMtJIyAa9msDA7wiRt/Dv99G6tO6YEBPv+WX2H3Y4/TtbzM4tgof/e5a3jgqf+75TFGEPCGXb/Jsy//KjsOHMR23eo19g9aPDp0BQ/1X0UgBkoMDqV2sCM/w22z9yNAPhe0Ny0rKOQU4JPN+MSWDXbsimxaWI7vjDJ1tIRTUtX4zuFRi3ji9CZaWI+Oq7zw2FJBqZT6hQ22K+C/naXuaDQEQegdmV7xEYGeXpO+AavpSzoSMdh9SYz0skepqIjGhEI+aDkHVtuEiNA/YNM/0FqYRWMG23bU539dWW4+N+ibJlO7d/PE9dcyvWsXLSdBm+BGozz51Gury2bExDDCOdxiof5kvmEweek+EOGbP/0ith0+wsTjj9O3N+B1LzaITPTxjj/fg2+sCSPPsDmWGGUqPsqOwkw4B9nh5IhSUMyHaf+6ezb3urJsYWJvDMcJ8L1wXvZsZF+CSlylFpQXCtoGodGUUUpx7HCJUmktOH9hziOXDRhvodGYptTFXOayPrms01SYJZKn7jsXf+tVLL//BxhOvTD2TZMD11zF9MTEKZ+jwtj2CJOHS6iyxuzHLfJ2gu895znhDiKc2LObE3t2A/CVJyH5YJF+M4ex7vo9sTiUHGdHYabt3GUzlIL0sk8m7ZPLBogRDmAGh+2OBF8kYoDON685BbSg1GjK5LIBJacxg00hH1AsBB2Z7JIpk75+k+VyDGRFtm7fuXnTIcB1t3v8zstex8N39QIQX8nwM8GjGKwJygDwbJvjZYF1qtzy7gIfuOflcOsX2HNJjNUVD8dRPPyrN/GNwlMJrNavDdXiGgWFrUITbTRmkOwyQs27Q80yn1u7XuWHSeRLRcWOiWjnF3YWeeCNj3DPnXDr52/e6q5oTgNaUGouanxfkUn7+H6YXUc1sZpWhGWnc1tDoxF6+gPy2QDDhFSXWdV8fAwCEWzVfJLuxjuvQZ5+G795/3RVOHJX+F8iXWRg1uUHP/F8nvLQN7BcB0GR7e7hnpe9FGWchMaqFPGsQ2LVwfQD3IhJKW7xF++xUXteSTRwuWblca5beZzl2y8huCt8ZRheQDzrIAoKKRvfDr+bQqq56qZsg1d99LmMzyseeOMjJBJCdrV+H5HQoSnoIDSmck9KxYBo7NyMcit87ntgaEF5IaAFpeaiJZ/zmZoMY27bxgQarbPbtCISMYj0r73AC0aUrw/dwLHkNhQwWFrmlvkH6XfS3HjnNWuax+eBzxeA3vr2ih4DM6FJc7V/lG/f9kpiuVUwDPLJLrqXFMWkSynReeyDXfQYOZrGKA8OBIjlPbpWSqG+KgZFM8r3+q6kYMaqxyVWSwxMZ6vLfXOwPJQg2x9HGcLceDfDx8tSsJwWb2kgwWs/7AE3Y972DF7+iU/h9hl0rSxi1IxOUimDbDbUNMv527FtcJqERotAqbT1glIpVRbaCjsiDSkENec/WlBqzluCQJHPhYHliYSBrJuvymV9FuZdPFeRSBoMDtvYdvhSVUrVxTq2wxBOqSSVAr60/VbSdoqgXCJkPj7AZ3bdzvE9vQSf3/hF37VUQGr7KkIx1RP2r7x+8HiG4/v6OnPkUYqRY6sYQX3IZbMjPcPise69pEoKwwsYmM42zEH2zecpJiN4UZNS0mZqXz+xnIsoRTFhVxMb2EWP0clVHnrmCxGlEKW44nvfZGBuKsxsVFTsuzRGLhcQBIpE0mRlyasmXV93CeH84xYSBIqpydKa05OAacLO3TH2323xx/w1n/mbV69ZBzTnJVpQas5LVtNeGCYhay/37Tsj1aTiK0sus9NrqcpWVwJWV0pM7I0QjZltg/lr5YwdEbaNR07JW3I6NkS2q5ugVNNGWV1KpotkBhIbtmG6QVMhVosRKGzHx41u/FhHC14oqDbcs9w2iiADiWzzrFeioHc+jxO3cKImxaRNoWudGTZQjBxdxVBCUBP1/+gNt/CMe/6JWCHHwHXdSNapG5j09lksL64TlOXsQq3SDSoVprBbWfIIVDiQGh6zmwpWzwvTDmYzoadzd42j0Ea1ShfnXYqFmnltBV4AM1MOO3aH86evv7TI25serTlf0IJSc84T+Iq5WZdMOUA/kTTIVQL9ayINpo467Ls0BgJzM83zeR6bdNh3Wbzt+eIJYWRbBCGs9rFZ6kypQHKlSP9sriH7jaEgUmohsJUiUvRQIrhRk0LKJlr0GjS5hsM6NPkZgSIcYnSYAB7B6Gq/ezzrkMg6KAEvYjKzsxtlrl11IuuUz9vY55kde9l++DE+tvP5nNg9wVeDD7H/7vD1ZNnCzt1RZk44oeYmYUmykTG7pYlzesqpq5qSywZMHiyx+5JYNVYVQo1w8mARr+bnsrzkU8j72BGD7GrYRiQqjG6LNOSuTTdJYwhhzGzgKwxTdFzlBYAWlJpzGqUUx47Uh2zkWmTDAchkfGKx5iniIEyK7ZSChhAF14qwPLwNQfEUa55IpPMsiDfeeQ1A/Txjbdux5o9ZIFCKN5p041mHgRNZpDwKCEyD+W0pfKsEXtBUWCrAtw08uzPBXoxbLSdmKyK0QiQKl84eZCr6FAopm7655m1WziwKrJJP73ye5dFUdXss1zxRgjJNSrEE+29+Fid2TwDwIuNtcEe4vSI0d+2JVZOyt5sDdJygaWmxStL5oZE1bTZ05Gr8AooFKBbWfmdOKfwdTuyL1mul7ZLC13y+fnA3UGi9s+a084F3zlC89QvV5WedQltaUGq2lFIxIJ8PsKzQCWK9iauQD+qEZFtU6DFpWu21KtdVRKJhMP/xow6z2yZ4/LqbkSCc73zCMHje7APszjdNKwxA7J6X87EnY+Hc0+db7gaAE7MoxS2ihTWNUAGBKeR6YnX7Wo7P4PFMnTAUL2D06CoLY0lsJyCxWsIIFKavUOVLVYYwt72740QDyjRYHk7QN5tHqNctPdtACdhOQGAIs10xPvz2gLf74NsmK4MJehfydXOm689qAMlVh+UaJUq16poKOPiUpzC/s6/p5hcZb+Pav1kB4M/tH25YpcMpqqZZkpSqF34AhULzWp1Nu6lgZdFjeGzNpJzqNkkvN7rpRmOi616eYe790zXL0P1Xv79he/Erp+9cWlBqtgSlFNPHXbKra/GGIrBjIlrnxVjqsDZkhWTKwLIEy6ZlLlHiUWaiPXSZOYafkuIbu28mMCyoUe7+Y+RGXjP5ZeJBietuD+1ytfGMvG9tX8vxsUs+bsTEi641YngBPQv50BwJlGIWEcdHFORTNivDSVRlYKAUsbxL11Kx3mmHNSE0OJNjYTTJ9N5QoNhFj2jBw7cMCil7U9l4ALJ9cZy4TWqliOmGc5v5rghOzFrLx1du8+G9++DJ8LjMQJxiyiaxWkICRfdyY07YZpQSNl0rjfsqMcj2tjeHV773W7kZ7riZrwYfAqiaZ2uxI60Tokdjgucp0ssehXyw6XqZ63+PQ8M2+VyA5ylUsPY7HttePz97/9Xv5551JnlN51z7khX+5Isfry7vv9vi/tMoCDdCC0rNWUMpRWbVZ3Hew20S2A9w/JjD7n3RqmktEpGOp9J6+kwi5TqT47uiHDnQ+FLOj43y6X23YSofX0x63FWauckEponzv17PXcNZ3rMunnFtJ8XQiQyxnIuS0ORYitvMj3eBgm2Hluu8Sk3fo5CKsLC9q64ZwwsYPZoOHXZUc89TCNvvn82R746CCG7MamnW7RQnZrFUYx6tP+FaT8J6m2s3wY1apIfCc0eKPrGCV9dvBeTXOfPkUxF8UzD9NSciBXiWUOjaXOKAFxlvCz/cQd18JoQJDWJxg+I6bVGM0Hv58IFiNdvQZhChwWRvWsLE3ijZVZ9CISASEbp7LUxTcEoBjqOIRIVIxNBxlRtw7UtWqp8/eNNYvZb4Fdi/heJKC0rNWWNxwWNpvn0dQ89VOI4iGg1fpYmkgW1LtU5jKyJRYXh0be4pGjXYc0mUuRmXfC7ANIX8+Da+95Rb8A0Tv6w+Lkd6aOb7GSjhk59c5fi+/oZtFXoX8sRybmgmLXcvWnDpm8mQyITra1s2VDj/aDk+XmRN8+yfyWI5G3u1AhhB6F26MpzsYO9Tx3R9+mdyfPBXbJCAga4MyyNJghonnaWxFKOTaSRQGCqce/Utg5Xhdd68hjAz0UP/bI54NlT38yk7FNSnEHdYO595zyvu4xtveozuPXHMY6vkVsPfWzQqjGyPsDTvNk1oUGuqTaYMEMhn1wlaWavuUndZRigcu8vjqSAI5zML+aDabjJlcLVvnPsVgM8i9/5pHPXg14AwOcP+t6x9t/dvVadaIGqzw6rzgMvjverOfXrkdi4RBIoDjxc3HMWLATt3R4nVmF89TzE77ZBdbZ1sfGw8Qld3KHzmov081HclS9Feep1Vblh+lNHiAp/c9WJyVmMoxnrnlWqfgZndPS3DLcafXMJs5sVZ6VezNg1YHEmR7ylrUEqx84mljsM0IBREx/f2NS+6rMIQEQA3Yp6SAJJAsf3gMsY6DdCNmEzv7qlrWwJFIuNgOV7VhNv23JUfwukKzFcKAkX/fJ7kSom4HSCieOrUw1yVfrI69/3jHxUIWviC7b0simFINSxkad5jedkjCCCZNBgabR5eopTCKSmCICwIXplSaBSyJkMjkYs2rnK95n+2edYPv/JdpdQNJ3Os1ig1Z4VqqaMNBKUhVLXJCpYlbN8RRSmF5ylOlAv+VtrrH7KqQvJEbIi7x56DJ6GQyFkJZmOD/NTMtygZm8yMbQimG+C2sAoabS6m5es/CL1TO6GVAEeEaNFrSBcXKXgMHc9g+KEkCEyD+e0pnPjJVSpOlucg1ycksFyfWN6lmFw7vzKEXE8UaPFlKUVy1SGRKREYQrY3tqksQq0w/ID+6SyJ7NqEtAAlN/yOvzVyPVdck8f41vFwmwE0E5QSJrivmPxFhIFhm4Hh9n0slQKOTzp4nqpcZlOUgpVln6GRTV3eeccH3jlT/VzrcQpbazo9Vc7fnmvOWZRSDe77lt3awQLWFIttO1onDxcRbFvYtSdGqRTge4pozKjzLnxg8Do8o/5n7RkW/zxxK55lEsu7DcInMCQ0GzZeSNvg/WLcbmiv3ThAAb4llOI1bYpQjFtN5/h8A8ygibBUCn+dR6XIQQ9xAAAgAElEQVT4QTnTzloPDC9cN7W3ry6esVPskt8ybtN2fIqdWn9VmGggUo4DVUAi47AymCAz0N6Jp5N27ZLfcmBiKPjw0nOZviPU4P6s53/z2APrPF0FurrMTaedq1SbaVtbs3b/soB+1Vs+BReAVvmBd85w+Z99trq8/27rtHqanktoQak5bRSLAbPloHCR0LlmaCTMcFIJ/8hlG93x4wnB90Lz7NKChwgbJiCPRo0G5eWmH7yD//uKKWjy4rKcgPltKUYn3Wr+UUUYsrA4lmRgJoeqMTEGArmeaFvtb2kkydhkGlUWsgpQBgQiWH5zCTOzq6fB3Lg0lmL0SBpRa3N8gSksjKYYPp6p84INha0ReqbWkMw4zdUZFW7L9sYat22AGzUJhKbC0o10/upIZJyqkITwuxcVzvHmeqLNTcgdEC14WE5rIVnB8tZUyN++8W08d+HL7Dl2BMNQeHmfaEwY2bZ57bbZb7kd8Zoyax+8aYxb7jr34yprHWzWh+YUt9jB5mxycVyl5ozjOgFHy7ULYa2GoOsoxneFEm1sPMLMibX5G5FQ0yzk1942XjYgn3PYtiPSUX7V2njG+H9bYchvNTcouDGb6d29dC8ViBY8nKjJan8cN2YxHbPpWcgTzzkEhkGmL7qhcPGiJif29JJaLhItuDgxi0xfDMsJNTmod9hdGkkS2I3X5EVMju/tJZkuYTs+Tswi3x1FGcLSSJL+2VzV28SzDebHG+MlDS9oCCuBUCCZXusEDe3IdUfpmc8jtQOIcn+Lic0Jylaaad9sllg+FKKFpM3ycKJaiWQjLGdjVa4SllNdNk3ufdlL+e7SMn0LC2R6e/j7wX9g/92bnyv1vQ7jewHDoM7Z7FylITaxRkN8YAv6c66gnXk0p4W5aadag7EWERqymRQLPseOOAQ13qLrsW1h9yXROnNYJZ7xXc9+LU/+exdObM1ZJZZzGZpabfpCDgTSA3FWBzfOqXq6sIsu/TM5bCfAs4TlkSSl5MlVD5YgTGcXmNLSQSeadxk+1nj9gcDcju6Tng80Xb/qpaoE8l1RlkYSmzLl9k9nSaVLDQOYika/PgnDiT29dV61rYgUytVPWs0Llv+fmehp0MBbsd7hpFQMmJ91KRYCLFsYGLKr8+GOE3DkQGlDYRmLw/YdsYYKNOtTHZ5trn3JSmgGvkg4FWceLSg1p4Wjh0tNE40bRqhJ1mqHxyZL5NukoatwyeUxnvWxa/n+7n187MkYP/zHLoanMlhuRW0UFkeT5LujjBxJEys25ndVwGpfNAynuJBLHynF8LEM0YJbFRxhijyLuR2dZ+w5E0SKHiOTjQKtmbPSZgc1I5PpMMF7k22KsPzXycyDfuCdM0z8+yPc9ZofNHivDo9a9PaHA4+ZEw6rLfK9QlhJZGJffX7Z9dz0g3fwvYXDfOzJ0IJxuuYuK2bT119a5KmHD2yY0ehCR3u9aracWFwo5BvXVxJK11LIbSwkPcvif/z0r6IqJaiUYtuxFaxKFQ0V/jMwncWNmNhuczOcEsj0Jy5sIQkgwtyOLlIrRVLpMNFCtjtKti+25dfuxKwwXd5cvjrAUagwucI6AWMoiDYZ8LRibkc3g8czxHONTlWebZy0s9Db3zfKrV/4NuOqPvRRKZif9ejpsxARRsZsEkmDlSWfwFe4rqqGn4jA6Ha7rZCEtfRrryov/8ntHvFXXh+28fTbuOXdnc1l3vOK+6qfH3jjI1WzaZGL22x6OtCCUgOEtRsXFzx8V5FIGQwM2psqVtw3YJFe9uti1ETCQOv1sWcbhYl4lsUT112DMtaOixY8TK8xKF8UpFaKuBETs9DkBSuCv8GL6oJBhGxfnGzfKXiSniGyfXFy3VFieY/AEAIDRo+uNuxXidPsFGUI8+Nd9M/mSKZLVUEcGBJq0qfA4PRM0/wASoWJMexIGE7S3ROGJx16slj3+1cKTky57NlnbupZ2n+3BXdXtL9H+OOabdfd7oW5b5uYTR+4QD1OzwW0oLxIKRYCHCcIU2vlfeZn1zLmOEs+mbTPxN7GeZVaXFcR+GGKLts22Lk7yuy0SyEfYBih1+tgkzi0nj6TlRyo4poWqIDAMECEQ1dcznef82zED8JcqCIYfo2rag1hXF/AylCiYY4uEFgZiG+5RqUJUaZRV6PSjZrYRb9eYxPI9G3SQ1eEpdEUq/3xau7bYsI65fue6+oikcs1rPdtky/+9av4QPcTVXNmPhvgNzOUKEiveAwMnR5HnkoxaLRQPKtoQXmREVZkdygW1tJrNdPufB8WF1xGxhodUDxPcfxYiVK5NqAAI9tsunssdu5unbOzkpHE8Dyee9eX2XZkksAwMIKA+bFRvvvc55Dp78PwDMaOZLC8ACWQ7YmSHog39eoMBAopm1LCZn68m965HJGSj28ZpAdiJxUWoTk7zO7oZmAmRyITFoN2oyaLo8mOvV7X40XMutSADShFarlIV7oECnLdETL98bXE9Ot45Kaf4Dl3fRm7plilZ1kcvOIKvv+1IW5lqJqg/d5P0dQxTSlwnQvPD+RiQzvzXGRs5HxQi23DnksbzXhHDhYpFesbECmnnosb3PSDd1TXt5tf6V5aomdxkdW+ftKDAwBECi4jRxs1w3xXBN8y6FoqVjWQitfk9K4evFNMDq7ZQoJwvlKd5rJUkYJL91IRy/UpJmzsUphRqNbZyY2aTWNbAVCKKx7az3Xf+hZG4AOKg1deyXee/zwCs14g983N86JPfgrLqzf/i4SDyJ7ezf8+XSdgedGjWFTEYkLfgHVShcQ1IdqZR9MxnQrJVpSKQdME5QHw/Ut28KVrXwodOh+s9vez2l+fdLxnodDUwSOZcTg+0U3XcpgvVqDq1DNybJXj+/q0ifV8xZBOisNsisRqiYHpbLUai130134zldOqMPtQPOvWmYShXNFlMk22ZyffesE4kVKBfFeC6d0DDRqo4QUQxHngtldiOyXGJn9MLtXNwrYJxBAmslM8a2k/cb++mk0QhM4/hhGmaawNhSoW6+OSC3lYWfHZORFtqGCiOfPob/wiYzNCcn1Kr+tu97j0r15OMdJojhUFzqPFU+0edotMK0qEVDo00a3PPWooRTzrnPK5NRcIStE/m6ur3tLqRWeoMAZ1PQPTWSw3CMukiYEbS2J6Qs9CvWu3+AFjR1boWinh21GKyW4OX3E9czv2Elg2vmFxoHuCu697EVe9cG0Sc3nJ5cDjRY4cKHHoyRJPPlZkarKIX84ZO3fCqQrJ6mUFYbyy5uyjBeVFRiLZ+S2P3jxK8P7ncHywh/2HhQ99eph3fziL6TWGYnimyfHdu065f6W41Vy7UKqa4m09EoQOPShVHt1feNMJms6x3ABp8htomrFJwpSA9StVQ7gJhEK1EnpTIZUu1VVXCU8k5ezra+edTUf4+dyv8J47fg3nPc9kfqUxq08uqzh6pIRSikKh+W+41XrNmUWbXi8yRsZsJg+VWjrxVHBtm3vdvVz70/+B6XnEgXg+z8DsLMf27GH8yBFsNxyJ+4ZBKRHnyade11EfTNendz5PPOcSmMJqX9npRoT0QCJ07qjRBgIhTDUXNQlWSo2B61IpCbWC6QcowvRrSyPJsByJ5qIiMKVl/tdmSQ5yPfUOaG1/Met+e7Vznu2QspkX4NvvepKRQvO4X6ekql7jzcqBGVq12RK29GsXkReKyBMickBE3t1k+xtEZF5E9pf/fnkr+nkhEYka7L4kxsCQxej1XYi5VnWoklHONwyeuO5adh44gOXVZz2xPI+h2Rm+dfsLmNs2Rrq/j8duuJ4vvf61OLGNPUwNL2DscJrkqoPpK2wnoG8uT99c6IbvlZ0rigmbwADXNlgeTpAejJPviuDZYaLuCoGEcXc9CwWscr5TQ4Ulogans6fra9OcRwSmQaGVZYJyKJKAZxnM7uxuSMquDMGJmQ3HK6DQVR/m4UYa92t6zrLjEEAi0/53WSwE9PSZDVPuItDVYxJoi8lZZ8s0ShExgb8CbgOmgAdF5C6l1GPrdv2MUurXz3oHL1Bqk4gD9F0zx3X33U//7CzFeIKpfXv48TVXk+/u5jUf+GDTNhKZLMf27WXy8ss2ff7upUJDjUNDQWqlRHogQWAZuDGLuZ3Ng8VndnXTs1gguVoChGxPlEjBZf2sqaEgkXUwvOCkq1Nozl9WB+LE85mG9QI45cTyXsRo6QC2OJZiZHJ1XUUXg+Wh+tpimd4YXcvFhgovlXNVlgNTyJfrh87uGCf16GMtNddsJmB8VwTXUeSyYRhXEKwVGlhd8enuNRkZtZF1FhPPU6yueHieIpE0SaaMTZcP0zSylabXZwAHlFKHAETkH4CXAusFpeYkqCQQB/idl71uLX/k++r3Wx4e5p6Xv6xpG4VEgq7Vxuwpnm03uMd3hFIk0qXmZgyBSMmnuIFQU6bBynAyzN1aZuzQcgsHoLByhhaUFx9uzApN8k3yyzpxCy/a+vdreAGRos/ycALDD7DcoK6iSy1+xGRuRzcD09kwc5QK59kDgXg+fAYLKbtuGuDhZ93Izh8fwHacpr/bQj4Ujtt3RnGdgPSKz+L82vOsVOi9joLR7ZGa43yOTYbTFpVC0dGosGMiiqGnIE6JrRSU24FjNctTwDOb7PcKEXkO8CTw/yiljjXZBxF5M/BmgBH73EvhdTaoxC82xC7edXLtPXLTT/CMf//PuoBr17J47IannVQoRu98vmWdRlSYm/NkcGJW85eOAu8kg9c15zeBaZDriZJM189pq3LS9Vaklgr0zdd7ts6Pd1NMts6sU0rYnNjTi+kpAoO1yioVJ4B1z0q2t5cvveG1vOxv78Rs4ShQKUNnRwxyTTy6lYLVtM/wqMIwBaUUJ6bqPWVVAKWiYnnx9GUGuljZSkHZKuF/LV8CPq2UKonIW4GPAc9r1phS6iPARyBMOHA6O3ouE7vn5bz9faPhQofxi51y4OqriBYKXHv/t0OvU+Dx65/KIzf9xKbbkkCFJqom2xThKLxtVpU2pAcTJLIOBI0OQKc7iF1z/rA0ksQzDbqXixiBwolZLI0k8KLNX3t20aNvPt/gnDM0tcrUJf0tM/gAYU7h9eke2wwms729HLniciYefxxz3ZxjPGHUaYCu2/p15vmKiCm4jsJvkuq4IlC1oDw1tlJQTgE7apbHgRO1OyilFmsW/xZ471no1znLdeWqAt/fvW9NOL6v/TGnhAiPPvMZ/OiGpxHP5SjG4/j2SdY13KB4sAK6Fgtke6ObqnUIYeqymV099M7niebDuo3pgXiDN6PmIkOE1aEEq0OdlexKpUsti1/3zuVYHjm9pdoeuvW5jBybIlosYrtuNapkdFv9MxaPG2Qzjc+PSFi3dSP0FOWps5WC8kHgEhHZDRwHfh54de0OIjKmlJouL74E+NHZ7eLWc93tHo+/6+f42JMx3nNXL3x+42MSmQzP+Np/MH7oMMowOHLZpTz4k7d25JXajMA0yXWfWiWGhli1MpX3UiLvESt4dC8XmNvehRcxNyUw3ajF/Pip9VFzcbPeyay6nnK8ZKBY3NbVUVum6yOBCq0kLSRVMZnki7/8S+x+/An6p2d5w/OXWfzyLOY6K8jgsE0uW18gWgQGh62qo44dESxbGvLKikBPr55+OFW2TFAqpTwR+XXgXwETuFMp9aiI/AHwkFLqLuBtIvISwAOWgDdsVX/PJg2VzzehNZquyx2f+HtiuTyGUhAE7P7R4wzMznLXL71+y4aXyhAyfaGHYK1pa733q3iKsclVEMgnbRbHurT5VHNWyHdFSK42xulC2Ys645Au+W0dgUwvYHAqQ6QU2kGVISyOphpS5FXwbZsDV18FV1/Fe19xHw/cPVfdppQiuxqwtOSVa1oqfD8UigNDNl3da/0QEbbviJQTFoTzkyKQSBn09utw+VNlS79BpdRXga+uW/c/az7/DvA7Z7tfZ5Mb77ym3pQKHWmNrdj9+BPYJScUkmXMICCVXmVscpLpiYmO20qsrnLZ9x+md3GRue3b+PE1V+PET95RamUoQSDQu9h8rhJqBKeCeM5l8ESG+VOsK6jRdEIxaVNIRUhkmnujAsQKLtlWglIpho+u1qdh9BWDJzLMTPTgtpgbbcXCrMvykl/nE2RZws6JKEaTwWM0ZrD30hjZjI/nhnOd8YT2+D4d6KHGWea62z1+52WvAwhDNk5BKDajb26+mjGnFgkCeheWOhaUA9MzvOAfPosRBJi+z7Yjk1z54EN85XW/ePJmWJFNlVAyVJj5xHT9ky69pNF0jAgL21IMnMiSbCYsm6W7qyFS9LHcxlzFoiC1XGR5NNVxVzxX1QlJKBeM9hQrKx79A819BQwjLCStOb3ob/QMU4lnfJHxtrWVJxmu0QnLQ4O4tt0gLAPTID3Q3+KoRm76l3+ta8PyPAzf5/p7v8k3X3LHaevvhohgeoEWlJqzgwgrw6EX9fokAoEIhTZhIma5fmqr4uKbobZebC1KQS4b0D+wqeY0p4gWlGeAlvGMZ4Ejl1/OU7/5LUzPq5pffcMg39XFiYnOkpZbJYfexaWG9YZSjB8+fEr9K6QiQGPV+PXZTNY2KNyTDBvRaE4G3zaZH+9i8ES2mlzdK2fzaTfH78StpvObCogWPHrm86wOtC4U/VvuVbyKRwAwLWmZi7kTT1fN6UULytPEmYxn3AxexOYrr301z/zafzJ+6BDKMJi89BL+6/nP69iRJzCNlvkrPfvUfjKBZbA4mmRgZmNhGUiYimyz4SIazalSTEaY2teHXfJRhnQU42s5Pp5phDmHy+sqSdjNQNG9VCCRdZieaF4o+uG7evngD97B/Ve/n1hcsG3BaeLF2qedc846+hs/RW76wTtCzfFMxjNuknx3N/e84mUtM4OsZ+ToMS7d/zC263Dk8ss5fPllHNu3lx0HDmLWlDDwLIsnrrv2lPuX643hRE1GJ1cbiukGRpg9xbdMVgfCROgazZYgghvr7BWZSBcZmMlVC0U3s5AYKhSmzQpFN55aGJ+IcvxoKSyULmFbI9tsojE9cDzbaEF5klRDOLZQe9yQDjTIa751P1d95yEsN6y/N3p0ikse+QH3vvTFJFcz9C4uokQwgoDjuyf4wTOf0fHpw5eCgzLChNC1OVfjeTd8+NeFiogKU4aVEjqTiOY8QSkGyoWiKzSZqgTKhaILGwtKCE2sE3tjOE5AEEA0KjrB+RahBeUmue52L3TMOc3eqltBPJPl6m9/B8tfq41nuy4jx6Z42f/9/zh05VP47nOfTaxYZHlokNX+zp2BeubzdC+tDSL6ZnMsbEtR6Aqz5dglv2UdP8vxtaDUnDeEyQUa17cqFN2Qf3gDy08kojXIrUYLyg6oCsfzlGihwGXf28+2I0fI9nTz2A03sDQ6wuixYyjDAL++iKwAsWKRy76/nx0HDvLPb3z9plLXRQou3UuFBkE4eCLL1D4bZRqU4haJjNNUWHZq7tJozgVUGy2vtlC0IrSYRPMOnm1QTFj0LhToWikiAbgRg0cfbq8xKqXI5wI8VxGLGy3NsJ6nKBYCLEuIxkJNVKkwH6xhoquJbBL9RmrBjXdew2+5VwGEqePOU2K5HC/+6CeIFItYvs/QiWl2PXmA+170QtxIpO1DbgYBsXye3T96nAPXXN3xOZMtcmYiYRKBfHeUXE+MnsUC4qk6551S3MLRglJzHtGujJuCBjtsKuOSyLr4loHpBdXBYsQJ+NPfX+AP/uk3mP6Zv2hoy3MVR4+U8DxVbS+RMti+I1I1ySqlWJjzWF70quEldkTo7TNZnPeouBx0dZuMbLO1wOwQ/UaqoS7m8QIwrQJc/e3vEC0Uqk45hlIYnseN//bvfO5X3xxqlG2omGI7EZSW42M7PkabUlqmF1agVYYwPdFL31yOeNZFCeR6oqwMdpbAWqM5ZxAh2x0htVqfpCAAVoYTFOMWo5OrdXVYDQXiBk2TE/zhh+d4U5PTTE85Dblc89mApYW1MlrZTMDyohemsSvv6pQUczP1pUUyq2Eyg207tLNcJ2hByflvWm3H+MFDdZ6rFQzfJ7W6yr/93M/y/H/8PJbjYnlew4PrmSarfRto1EoxeDxDPBc66NAitjqswpCne6nI4liKYtLuKMm0XfJIrDoogXxXtG2uTY1mK1geTWEGGWLlQZ8oyPZGyfTFSKVLrb171iFAsNi43vcV+ULjg6UUpJfXymhVhORGKEWY6s5T5TyymnZctIKyIfH4BUopHoOVxvVGEFCKxigOJPncr72V4WNTPOfLXyGWL9TliVWGwYGr22uTvfN54jk3NCGVD635WKUSCmJ4AUNTq0zv7t0wPq1nIU/3YqFqyu1ZLLAymCDTpviuRnO2UYYwP96N6fpYboAbMasm2cCQjgWlAszhJutV6yaCmufVb2XNaYIIWlB2yEXlTnXjnddw453X8J47fu2iEJIAjz79Btx1SQJ8w2Bu+zaKqSQQCsPZXTv5yut+kbnt2/BNE880Sff18m+v+lkKXe1zVKZWGisuVB69UtSoc2iobi/nv2yHXfLoXgydgqpCVkHvQh7L8dseq9FsBb5tUkrYdfOWhWSkpZAM1j0YSiB2U+Nr2bLCMlrNSHWZBIFiacHF9zoXlEpBJKKFZCdcFBrlhRTSsVkmL7uUvrl5rnzwIQLTxAgClocG+fpLfrph33xXF//66p8nWihgeD6FVGeFao02tp5oqbkdVgDbbS/s4hmnuVMQEM86ZPq1Vqk591GmML+9i+GpDFCvGWZ7oiQyDmagKEUtlkcSHHvAJnbPyzn+gv8gbXfR76zQ62YZ2x7h2GQJyvOPImGqu4FBi6OHw8QEnZhdK/T2m9qZp0MuWEF5sZhWKwxPTXHZ9x8mWigweeklHLzqSgLLAhH2P+dmHnv60+ifm6OQSpEeaJ9RubTJUlrFuE0s7zZqjW2OCQSKOlZSc5FgOz5KqFpeKs9GIutwfF9f3YBU/ID3/kac7p3PR3kBgRiM52e4bfYB9uwT0iseTkkRTwjdvRaZVb+lkEwkhUK+vQBVSlEsKPI5H9MUunrMhuLRFzsXpKA83jt0UQnJa79xH1d/50EkCDCAbUcmecZ/3MMDL7iNQ1ddCYATjzOzq7Ok6O2QQJFMF0lkHALTINMXY2kkERZbblEhfj2KMJ9stifWdr98dzQMIWnykOvUdprziWS6RUFoX2GX/LrY4f7ZHJGih6OssKQ9MJUY5bv9V/KMpR9UHXcq5DJBU0FoGBCNGhQLftPtpaJCKcWJYw65bFDVUudmXcZ3RkgktdNchYtqjvJC5Mr/+g7Xfvu/MMtCEsplfXyfm/7l37j0+/tP27kkUIxOpumbyxPPeyQyDsPHVonnXE7s7sXZwBtVAZ4VCtfpiR7UBqNWL2KyXC72XPu3NJLUZbc05xdtfuqqdptSJFcbE3H4hsWPuvc0Pd5qY5iJxozm2qRALC5k0n5VSJZPjwrgxDEHtRk77gWOFpTnMd2Li1z/jftaPoNmEHD9N7+FNAkPORmS6SKW49eZjwwVer0GBmR7Iq0iQ1CAEzM5vq+P5ZFk2yDtWrL9cU7s6WVlKMHycJITe/rI9bbXRDWarcTwA7oX8oxMphk4kSGadwlEGvx5Kpl6BmZyxLLO2soWONJcIvb2WU1dCQwDuntNkimjYbsh0Ndvk15prm0qFdbE1IRoQXke89y7voxsMOozXZdIsb13aackMm7z/Kwi9Czk6VsoNnVhV4Qu8otjnVd4r8W3TTL9cbJ9MXxb/2Q15y6GFzB2aIWexQKxgkdy1WHk6CqxgleXyq62ukis4DF0PBN6gRuCE2tuLVEifK/3iob1kajB2HgEwwiFo0iYjWfHRBQRYdt4hL4BC9MMtyVTBrv2RFt60WoauSDnKC8G4tksPQuLG84JKsPAiUZPyzl9S5qGeqAUXcululFX5UXg2QbZ7ijZ/hiBriupucDpWSxg+mtz9c2ez8pgcn0Jrr75PNneKIujKcaOpBuOV2Lwvb6ncFX6x0RUfaadrm6TVFeMYkFhGBCpqTQihjA0YjM00qiRdveaFPKNc5wiEIvr57WCFpTnKan0Kr5tYTpuy31cy+KxG56GMk/PfF6mL0ZiXchGs7p7leVAYG5Hd0dFbzWaC4F41unIoa3pPkqFyQpiFk7EIOo0yailApYjPYyUGtP3iAjxROuzB4EiveyRzQSYltDXb9LdY5Jd9euceSBMbadLeq2hBeV5Srq/D8NvktKq/L8bifDo05/GIzfdeNrO6cRtlocT9M3lq8Ni3zRQKCLNAp1FML1AC0rNRUNgGuCe3NyeAEHZwc2PmCinMRdsIAYJf/M1cINAcfRQCcdZCxXJrvoMjVhs2xGhWAjI5wIMU+juNjF1tp46tKA8T3HicZ689houeeQH2F5ohgkAzzL56mtfQ3pwsKNkAa0w/ICuxQKJrEtgCpm+GPmuCNm+OLnuKNGiRyCQSpdIpZ2WJlknqn9imouH1f4YA9PZurn89VaXZlaYQKCQilSnJ1b748Rybp31xgh8RosLdHn5TfcrvezVCUkIHXbmZz26ey3iCZN4Qg9oW6HfYucxD/7krWR7e3jKg98lWiwyO76d7976XDJ9fYwcm0KUYm58O8EmTa/iK0aPpOtLABWzRAoxVkaSKNOgmIyQWi6QXG1uagoE0gPxDUNANJrzkrKZ1LcMVE12m3xXBLsUp2epEIZ9qHCeXokQKYWZqEpxi0LCome5COX0jIVUpM7ZrZSwWRxNMpEpUFotEWAwXpjheXP/dVLdzbaItRQJvVuTKS0k26EF5fmMCD+64Wn86IanVVeNTh7lhX//D1VvWCXC11/6YqYnOk82kEoX64QkhM4G3StFVgfi1dCOruXmQdQKWBxNke85PU5EGs25RGqpQN9CoVrHKtcTZWmknO5RhPRQgkx/jEjRx7cEt2xVMfwg9HitaP2CUoMAACAASURBVI0DCSw3ILCkpaNbIiXkMyYpL8+lmSNEg9Y+Ce1olWlHoYs4d4J2a7qAiBYKPO8LXyRaKhFxHCKOQ7RU4tZ/+iLRfOfmmliueRhIIBAtrHnbGS3iM5VAKaHHYJoLj8Rqib75PEagMFQ4gEymS/TN5ur2C0yDYtKuCsnKOlUrEA3Bi5pNhWQiXWRgJsf8jI8Sg4yd4t7hZ3IwOd6wbxAoFuZcDj5Z5OATBeZmnIYqIr39ZtOZGNMUYnEtKDdCC8oLiF2PP0kz+4oomHj8iY7b8S2jadyzqDBEpEIhGWle9sc08DtMKKDRnE/0LBQaBpGGCufqCU5fJpu++cbzeIbFdwauqVunlGJqssTSgofnKjwPVpZ8jh4u1WXWSSRNBofDxASGAWKAZQs7dmnv1k5o+zYTkW4R2dtk/TXN9t8sIvJCEXlCRA6IyLubbI+KyGfK2/9LRCZOx3kvVCKlIqbfWJHD8DyixVLH7WT6YvVptSinn7MNnJqclOmhBL4p1VJBilDrXBzrrOqIRnO+YXqtPVqN0yUolWp6HsspEZudYzXtVTXGQj6gWGh00nFdRTZT30b/oM3ey2KMjUfYsSvKnkuiRKJ6QNsJLe1jIvJzwJ8DcyJiA29QSj1Y3vxR4PpTObGImMBfAbcBU8CDInKXUuqxmt3eBCwrpfaJyM8D7wVedSrnvRCJFAokcjkyPT0EIg1lr3zb4sQm5ijdmMXCWIqBmRyCAgVu1GR+e1edAPQtg+k9vaSWi8TyLm7EJNMXx9sg56tGc77ixK3QG3XdemVINbRjIyIFj96FPHbJw42YpAcTlGor6YjgWwZWjbAcPnaQyx6+H0SYVR5KuYyN27hO88ogKoBC3qeru/5ZNE0h1aWfz83SbiLpPcDTlFLTIvIM4BPy/7P35kGOnOed5vPmgbOAuq++u8nmTTZJURRFSrYoWhdlk7JoW/IphzyrOcLh2R07ZmxPhHfCuyFrvCPPyrujCXs9Csv2SD50UiIpiqQo2xRbIps3KbLZN7u6urruwg3k8e0fCaCAQqIK1Udd/T0R3QUkMhMfEon85ft+7yHy+0qpr7F8B6VOuR04qpQ6DiAifwvcDzQK5f3Af6o+/grw/4qIKF2tFwgsxTsfeZQ9bx5BfB9Rql4eq/YFObbN2BX7mB4dWdW+i+koY6kIdtnDN6VtEXLfNMgMJMhc0CfRaDYHc4MJRgoL9WhVqBbqH0p05EWJFhyGTmeQ6vaW6xI9nWF6e4pi12JHnPmBOH3n8hgKYoUsV7/0NKYfeItq8nl2zGFo1MYwYGm4gAhEItpavFgsJ5SmUuosgFLqGRG5G/i2iOxg2dK9HbMdON3wfAx4R7t1lFKuiCwA/cD00p2JyKeATwFE04MXYXgbnzu++zi7jxxpcrfWf7xAPp3mubt/klNX7T8/V6hIvf2PVfEwHR8naoYXNFcKw1P4hgQVlzWaLYgTs5jY3U3PdIFIycW1TRb645S6Omv71jtZCJ3j7D2XbxLKWuH/nqkCg2dO0u6Sq3yFGLC0G4EIpLqbb26VUvXqO3pecnUsJ5RZEblCKXUMoGpZvgf4BnD9RXjvsG9q6dnQyTrBQqX+HPhzgNTo/i1vcVoVh72vv4EVMicJweRzrFjk1NVXXdD7iK8YPBN0QECCgJ5cOspCfwxlGPiWQXK+RO9koZ6SkuuJMjek5yk1WxMnZjG1I31e20bKbuhyy/FpqiFHIJb5nhgDE3ZoByAFKCXs2hvl7FiFUin4/UUjwuiOSFNKSC7rce6sg+soRIIo2MFhWwtmhywnlP8aMETkutq8oVIqKyIfBD5+Ed57DNjZ8HwHMN5mnTERsYBuYPYivPemRHyf7cdPkMxkyKVTKwqR5TgtP77V0jeRI1qopotUbz+CajxBcJBrGUHOZcM2XfPBa3PD59ctRKPZqnhm89xjDbWMl3Tsyiu4/tlDGG6zyArQlTKIRAx274vhucHUi7Wk/Fyh4FX7S1bfSwWRsb4PI9sWrdhKxWf6nEs+72EaQk+/SW+fpcWUZYRSKfUSgIi8KiJ/DfwxEKv+vQ346wt872eB/SKyFzhDIL6/tGSdB4FPAAeBnwO+t6XnJ5Vi5K236J2aIdPbw/jePSgj+AUlMhk+9KW/JVIqY/geClm2z6QCJrdvuzCrzlcklxRBh2Yz33Jb61EaKhDL+cFkU9USq+JheD5O1GpartFsdQzXx/AVC33RltQPRVVAnfC6yDOjIxy74Xquf+0lvGq9ARHo7TObolbb1WedmXRbAn6Ugsy8x+CwwjQF11GcOl6mOg2K7ymmz7lUyqpJTC9XOskKfwdBtOnTQAr4n8BdF/rG1TnH3wQeBUzgC0qp10TkD4FDSqkHgf9BEER0lMCSvBiW7IbELpf5wJf/nvTcHIbv4xkGpWSSR37545SSSd797UdIZHNNEa2eCJ5hYFYFsxbE44ngWxY/+ql7LmhMotSKs9HLyZ3hKTxDMFyfwTNZIiW3Xkx9bjBBri9+QePTaDY6huczMJ4jVnACV6kIhaRNMhconlT/WY7PyMkFxvf1hMYA/Oh99/Cvr3qV5x8Lnnf3dF6btVJp8yMWyGc9PA8Kea8ukjVqYjowqC773pWdCKUDFIE4gUV5Qil1UVpfK6UeBh5esuwPGh6XgJ+/GO+10bn1H/+ZnplpzGpHENPzMDMLvPPRx3jq3g8yOD7ekvZhKkUxFmNucID03DxOxMaJRJjcsYM33nYL+fT5zaPUUIbg2gb2Ct0QwgqiK5F6cYLBM1mitca11Y/QO1XAjZqUkvpuVbN1GRxbPPeD81+RyAei2SiHQhAP0DVfIjOQaN2RCEO7DEa2rb7iVSwm5JxWsVQ+nD3jLDbIDEEEymUfq03U++VCJ0f9WeCbwNsJIk7/TER+Tin1c5d0ZJcZ+15/oy6SNUxfseP4idAiAjWUYfDYx3/h0gxKhNmRLgbHFsPZQ7uELFnuC8wPxoM2W45HpOSGumfTMyUtlJoti1UJP/eXTmXUMIBIsX0t13uN3+LJLzzFwU++vKpxDAzZ5HPl0HxLYFmvkVJgX+bWJHRWwu43lFJ/oJRylFITSqn7CYRTcxFpO9+oFLFCgXwq1XI+e4bByQuMal2JUtJmYk83+XSU8jKFBMoxC88UylGT6W1d5HoDt6rpqrb+2eWqnGg0mx3T9VEhMQJ163IJiurv5SITjRns2hslnjAQCUrXddJQSATicUNX76EDi1IpdShk2YUG8miW8Nb+K9nzxhuYDWWwfBFcy+LDf/2l+g/OE8FUCse2KXYleeldd17ysTlRi5ltQQRrcq5I/7nFAusKWOiPkRlMttnWDL1j9YFil936gkazRahEzXrKVCcEBQiWuXlUisJ0Bd9TGKtsXxeLB2JZ4/iRUkvh9KaxCHSlTIa36d8o6DZbG4ZDd/8kw6fHiJZK2I6DY9uYnoflOM1mvwhv7d3Lyeuu4dRV+/Gttf0K871xynGbwfEcdsVDgGTOoZRym+rA1lCGMDeUqOZZVru4E3Ryz+hgHs1WRCnSM0XScyVEtZ+uCN20TTT49mPHeeejj/HVz+XxHUUyZTCyLdK2fdZKpLtNZqdbo2FrGAYMjtjnvf+thhbKDUIpmeTr/8sn2XP4TfrOTVKKxznw9MGWL8j0fSzP5cR1167LOFGqSSQBImWP4bcWOLOvNzRiL9cbx41YpGaLWK5PMWmT6YuHV/jRaDY5fRN5kpnFXq0NKcjLCqYvkK1W5Gmk99wk7/nmt7Bcl1q0Qj7rM366ws4959fztW/AIpf1qJTDa8V6HkyMV9i5W/eUBS2UGwrfsjh+/XUcv/46+ibOceOPngnO2CXEVtFb8mITLbpYjtf6g1e0j9gjmOssJbUbR7O1MVyfrky5KWCnMai00bqsL6suKHZFyPa1CuX1zz6LseQ6oFTQOaRS8c+rpqthCLv3RclVBTeMQs6vlsjTVqUWyg3K/EB/6PyGa5qcvmLfOowowHLalMxTYJfdC64EpNFsZuyKh1+NI2hEgIptgCHYFQ8UlGMmmb44hgqC4dyIQazgkKxWvcp3RyklbNJz8y2pYRD8zFxHETnPwHERIZU2Q4uq11iN23gro4Vyg+JbFj+6527uePx7mG4QYu6L4EZsXn/7baven/g+qbk5KtEYpa7wwJtOqETDTxkFJLMOycOzlBI2M6PJth1HNJqtihsJD+BRBC26ZralMFwfJOi800jfRI7kwqI1mshWyHdHObdjB32TUy1pYkpB9CJEpKbSJgvzrTfAiaSBoa1JQAvlhubE9ddx/TOH6J6bQ5TCUArTcbnumUO88JPv7ng/uw6/yTu/+zim62L4PpPbtvFP9/00pWS4m3Q5nJhFKWETq9V/pXX+JVZw2HZ8Hidi4Fkm2b5YfZtY3sEzDfLdUT1Hqdk6KEUyUyG5UAo66HiqKQhPCWT6g+C1sPPeLrkkF8pNpe1EQXKhzJs33cL+V15FPK+5pnLaaFu2bjUMDtsUCj6uq1A+SGD4MqIjXuvoK9UGZvfhN+nKZpvcLrbrct2h50hksx3to/fcJO9+6BFixSK242B6HkNnznDPV7523uOa2pFioT+Oawle9Qxq/LkKwY88WvZJ5B0Gx7KMHp9jcCxLerZEz3SB7cfmiObbJ1drNJsGFXTY6ZvIES+4WJ6qz0sqgjSRyZ1pnDbeGIB43gktRBBEits8de8HQKQp0yqX8Snk2xcj6RTTEvZeGWV0e4S+AZPhUZt9V8WwdT/LOvpIbGB2Hj2G7bSKiW8aDJ8eA6BnaoqrX3iRXW8eaekuAHDtc8+1BAKYvk/3zAw9Uy1tPTtDhMxAgjNX9rHQH26VNgqnocB2FEY1PcRQwb/B8Szty4VoNJuDaNEllnearUECK3JiV5qze3soJ5a3zpQsBvWELb/hR4cwlGr6XSkF58Yvzs1mbb5ycDhCd4+lXa5L0K7XDUwxmcAXCZnIF8qxGO/+1kPsOnIUAN8w8E2T7/zix1gY6K+v2bWQCQ0E8E2DRC7H/ODABY3R6fCuM+xnJ0oRKXlU4vo01GxeYoX21mAyW6GygkgC5NNReqbCo9nz6SgDExOhr1UqCqWUboV1idEW5QbmzQMH8JfUmlKAa1kksjl2Hj2K5bpYrkukUiFSLHL317/RZKWd3b0b12oNqrFcj5nhoQse49Ju7bUxdoQKv4vWaDYTntn+MprIljvah28ZTI924Qv4RvWfwPS2FL5lUImF5zNqfVwbtFBuYBYG+vnBhz6AY9tUIhEc2yafTvHYx3+eq15+GdtpdrUaQDKbIz07V192+JYDVGIxPGPxq3Zsmx+/7VbKidUH89RRQaeDvolca9HnpasSLp6+KUGJO41mE1NIhednCEHtVqPDmsbFdJSx/X1Mj6aYHk0xtr+PYnXfr932NpwlVbhqPSm1NXnp0T6vDc7Ja6/h9JVXMHB2Ate2mRkZBpGWeccaSgSjobFcJR7nW5/4NW744Y/Yeew45ViMH7/9bZy85mog6INpVyoUuro6vz1ViuG3MkRKbqhFCVVxrO7OjZi4VpAjVk/MEmFqe1rfEms2Pb5l4BvSVKe5ESUEXp6Gc78dypC6ODby2u1vJ5HNcc2rL2O5HkpBqttkYFhHpq4FWig3AZ5tc27XzqZlx6+7lu7ZOawlATxuxGZ+oHnesZRMcOieuzl0z931ZXa5zF0Pf4cdx0+gRCjHohz8wPs5s0wxA/F8LMcnUnKXFUkA3xCmtnfhWQZuNdovUnKJFhx806CQirSta6nRbDYyfTG6p4vNKSHVvzuPzDUty/dEmR1KBjkYnSLCsz/1Xv6PPzZ4+lOvYllQLiuyCx7xpIFtB+9cLvkszLm4HqRSJl1pQ1ucFwEtlJuUw7fczJ433qRnZgbbcXBNE2UY/NNPf7gjK+29X/sGg+Nn60nMVs7lJ7/5LR75lV9ibmiweWWl6JkskJovgQjiq7bVOmqW5MxoF+UlvSYrMSu0cLpGsyFQimjRxXR9ynEbz+58ZirTFydWcIgWFps0hwawAcn5MobrM71jdY3VH/b/lOf/rYUYcPJ4OaimU1XjdI+B8iGzsOjmzWU8orPCrj1RLZYXiL5qbVI82+aRX/lFdh49xsiptyh0dXHshuspprpAKQYmJth24hSVaIST11xNKblYjSc1O8fA2YmWSh+m53Hds4f4wYc/1LQ8NVciNV8KLMhqoFBYaSsFFLps5oeSuBE996jZPFgVj6G3MpjVWm6iINMTY34o0dn0gCFM7kwzfGqBWGn53EaDIG/SdLyOq1c97P8pLz5ioZTizKkK3pJMsIW51nlQpaBUUJw9U2HbDl3c/ELQQrmJUYbBW1ft562r9jcsVNz18HfYffhNTM/DNw3e9o//zPfv/5m6WzWZzeIbrXfLhlKk5+ZblqdnSy1u1jCR9E1hentKzztqNh2DY1ks1286r1PzJcoJi2KqQ5ERCU0TCUMJWI4fKpSm4zA8dgbfMDi3Yzuf/Q9TvHh3cKmenXZxnNXlHmcXfPI9HskuffN6vmih3GLsOHac3W8ewa7OXRpucHf7kw9+m7/7zX+NZ9vMDQ60WJMQFFyf2LmjZbnhhUft1QN2JJiTnNyhg3M0mw+r7IV2xDFU4E3pWCiBYiqCXSkuO39f27cT4nXZdfhN3vXwd1AimJbCLjvMfi1CImGSy3pMT7YWFemEuRlXC+UFoNNDthhXvPpaaDUfJcJItZpPOZHgjVsO4NiLEXO+CK5t8/ptt7Zs225e0bUNJnekObczzZkrenE6nH80PJ/kfInUXBGrcuEluDSaC8HwVdsWGYa3SuutN4ZnGvjV/YWmRQG5dGut4+RChnc/9Ai24xCpVDALDr4HZ05V8D3F9OT5V+FxV2mFaprRFuVWY7nQ84bHh+5+D/P9/Vx/6HkipRLje3bz4rvvaprLrDE3nGT41EK17uSiJTk7kqS8yh6TsVyFwTOLdWp7KJDpi7MweAE5nRrNBVCJmTR3jQzwpX2OZDt80+Ds3m5ScyXieQd8he36dcH1jaA4eqYv3rLtvtd+jIT0u1JALuvhVM5f7BJd2ia6ELRQbjGO3nA924+fCLUqJxpTTEQ4euAmjh64acV9VmIWE3u6Sc8UiRZdKlGTzEBi1RGs4gfFo5e6pdIzxXrroXLCDi5O2oWrWStEmB5JMnA2V78Z9AVc2yTb2ypoK6FMg8xAgswqq0NGi0WMsMaQCjwfIlGhVAwXS5H2ZZNNE/oGdL7lhaCFcotxZt9eTlx7Dft+/Dri+/iGgQD/eP/P4Fsrf909U1Pc+o//zND4WYrJBK+843aOX38dTjTopXchxPKVsBt3BEgtlIML1EKZ7hmTid3dOs9Ss2YU01EmoiZdcyUs16eYtMl3x9b0HPzfPlHgsRdAhWhlMmkQjdqMnao0CaIIDI5YxBMmmXkXpcC2hXzOw3Uh2WXQN2BjXYR2XJczWii3GiIc/OD7OXzLAbadPIUTiXDy6qs6KleXnpnh3r/5MqbjYADRUok7HnuceC7Pa3fcfuFDa3PHu7TTiFX2GDm5QDFpk+uJ4eoyd5pLiVJ0zxRJzZYwfIVjG5Cw6Z4qoAwhn462PweVQnwVCKqCroUSiWwF3xByvXFKHU5NHLhvnpOfOkkiYVDI+3UxFIHuXpNI1CAShR27I0xNOJTLCssW+gctunuCy3hsZNFNrC3Ii4sWyi3K7PAws8PDAPSfneD6Z54FhJPXXl1fvpQDP/hh0Ny5YZntuBw4+EPeeNstePaF/fiKSbujiukGEKl42BWP1HyJ6dEuimmdB6a5NPRO5umaX2yaHHF8+iYXO3mkZ4vMDSXINbphfUXfZJ7kQhlRQWCbAizXx6hWq4vnHTL9cRYGlr9JPXDfPB/7l18CEbbvipDNeGTmvapIWiQb5hcTSZPdV+gbx7VGC+UW59bv/xPXPv9C0KtShGuff4FX3vF2Xr7rzpZ1B8+Oh7bkUgJdmQwL/f0tr60GZRrMjiTpm8g3WZftnEK1BtADE3lO63lLzSVAPNUkkvXljY8V9E4WKKQWI1UHzuaI5yr17WzHbyrCUTt3u2eKZHtiLRGuNb7/mThP3/j5xfcSId1tke7Wl+aNxLqEQolIn4g8JiJHqn9726zniciL1X8PrvU4Nzs9U1Nc+/wLWFUr0VAKy3W58UfPkGroMFIj29MTuh/D8ymERMOeD/nuGGf3dFNpcGWtbGQqIqXzyx/TaJbDcr32d2pLiOeDADnD9Uk0iGSNsN0oCfpVhvHkA0/x9I2fXcVoNevFesUM/y7whFJqP/BE9XkYRaXUzdV/963d8LYGO48cC+0yIgp2HjvWsvzld96BuyTgx7UsTl57DU4sdtHGlZ4rYVe8ppqY7VpxAdW+ldqa1Fx8XNvsrIGqLHbDsRx/FX1UBT+kX+WTDzzFwU++3OlONOvMegnl/cAXq4+/CHxkncaxpfFNI1RgFISWsDu3aydP3ftBCskknmnimiZHb7iOg+//KcT3SWSzgQv3ggalSC6Eu7pqzWqXjtWzDN23UnNJUIYErtGVhE9V59gBJ2KEiuvSRYrgfC4lFm8+v/+ZOJ9+6PN1kVRKsTDvcvpkmdMny2QWXFS7PA/NurFejvBhpdRZAKXUWREZarNeTEQOAS7wGaXUN9rtUEQ+BXwKIJoebLfaZcWpq6/m5h8chCW5WbXi52f27SPb2+xuPXXN1Zy6+iqixSJOJIJvWVz1wovc+k9P1cveHb75Jp77yZ+gd2qaRC7H7PAQhVRnqSNGm559NbI9saBLSRUlwuSOYN/RgkMs72CXA7EupqLkU5HVtSvSXD4oRWq2SGq+jOErCl0R5gcTLfOF80MJPEvoni1heArPFExPNVmN09tTqKplqEyjfp7WbvhqRTiUIjA/VHCjOjMS44pXX+Oj14xh/cMRfvDtxbZXSinGT1fI5xajXIsFn1zGZ9vO5kIHvq/w/SAnUncCWXvkUt29iMjjwEjIS/8R+KJSqqdh3TmlVMs8pYhsU0qNi8g+4HvAPUqpVp/hElKj+9XbPvG5Cxj91mH/Sy/zjseewPCbCz77IuR6uvn6v/hkPUimd3KSa557gWQ2y5m9ezhy001sP3mSux56pF47FgJ3rGPbWK4bNIr2PI7eeAM/et89KwfcKMWOo3OYS0qDKaDYZTMz2sXIyXlMR9Vdsr4leIZgV/ymYAlfwImanNu1ipxLX2H4Ct8UHRy0xRk4k20KuAm8E8L43p666LXDdDziOQclQf3WFvepUnTNlUjPlTC9oC3X3FAC1zaJFh18Q4gWc/zMV75EZKGEUiAGWJawe28U0xIKea8lLxKC03LnnijxhIHvK86NO2QzwU2qacLwtghdKe1hWS13vfrQc0qp285n20tmUSqlfqrdayJyTkRGq9bkKDDZZh/j1b/HReT7wC3AikKpWeTIgZvYceQoO4+faFpuKEU8l6fv3CSzI8PsfuMw73r4Oxieh6EUw2NnuPb5F/BMs0kkASzXxXTdJuG94rXXmBkeWrnSjwizg3EGJgr17WvXifn+OL3n8liOao4edBUmrT0wDQV22SM5XyIXUhKsCaXoPZena6EcPDWE2aEEhe6LN/eq2ThYFa9JJCE4lwxP0bVQJrvM+WK4PolsBcNXlBI2fthNmAi5vnjoeVeq9mG9++vfxa6KJASFBJyKYmrSYWRbpClfshGloJD3iCcMzo41W5yuC+OnK3Uh1awN63WkHwQ+UX38CeCbS1cQkV4RiVYfDwB3AT9esxFuISKO0yYiT4iWSojn8c5HHwuiY6u/SMt1SeTydC1kQve5dH+243Ldcy90NB7LaXZrCYHbKpmtkMhWWvbdrgkuBGKZzFZWfM++qkgaKtjG9BT9E/mgWpBmy9EuStpQgQu/HbF8he3H5uiZKtA9XWTodIaB8Vz7+nBtePx/txgZGwudy1y0DiXUqSESvOY6qkkkaygFs9PnXyBds3rWSyg/A7xPRI4A76s+R0RuE5G/qK5zLXBIRF4CniSYo9RCeR6cump/SzQrgOH7TI2O0jMzg4RcCEzPQ4UE/bTDLgfWmngeA+Pj9J6bDL3ApOda+1saiqa5ydUQesffgHjhAUSGCvoQ9k7kg1qzmi2Da4eftwraNxVXisEzufrNlBD8jeeCG7hO+JPfmeDTD32eH9628tRPqrvNOCR4zXFU29mBCymQrlk96xLMo5SaAe4JWX4I+BfVx08DN67x0LYkR2+8katffJmuhQUs18UHfMvi2ff8BG40QiUaDS/GDGR6ekjPzWE1uFobE6treIbB6SuvYMfRY7zroUcQpRClKMXjfO+Bn2V+cLFCdLuAHvEhn7JJZpst4NraYdcMX4LWRsthtumnCYsCnciVO5q70mwOKjELN2Jil5v7TKrG80UpEMFwfeyKh+mEt3wzFCQXyhSWqQ5Vq65Teqi6jSEkkkE5ukZEIF0VSMsSduyOcOZ0pX6Si8C2nRFMU4hE2xuyMe12XVN0+YfLADdi8+1f+2WufPlVdh05SimZ4I1bb2Zq+3YA8t3dzA/003dusqkyj2PbvHLnHWS7u7n1n5+ib+Ic+e40p6+8ght++AxmdT7TtSwqsSjHbriOD37577Ea5jQtx+EDf/v3/MO/+Zf4ZnCBKMcsYiGusUrUZG64i2hpAdP1EVWNJDSkWlOzHlBYJ9MXp9S1fCsk1zZCi7HXqM9dzZfI9ut2X1sCEc7tTDNwNkcs74AE58HMSBdWxWfodBa74tVv+nwjuFE73/Cu/9t+lYMNz5VSxBNCId80JCJRYWBosRRkImly5dUxisUgUC0WX4yKNU2ht99ibsZtEkzDgP4BfeleS/TRvkzwbJvDb7uFw2+7JfT1J3/2ft73918hmcnWI1lfv/VmTl21H0R4/OcfaFp/anSEq156Bbtc5uye3bx54Cauf+ZQSz+9QIQ8zyHzmAAAIABJREFUth8/wen9VwLV/pZvhfe39C2D8X09JLIV7LKHEzUpdEUQFbhPIyUX3xCcmEUpGcELcbGZjkfXfBnL8Sl2BW275gcS9EwV2naeNxTECy7ZkCp94vkksxVMx6cct4JC1zpids2xKh6JTJDqUeyKUI5by34PvmUwuTONeMFNl28ZREouw6cW6udBbWuzetqGNloWyHeHW5N/8jsTlO7+GgcfWlzmVHzOjjuUCkvSsizYtSeCYTaPWURIJMLdsANDFnYEZqc9PE+RSBgMDtvYEW1RriVaKDUAFFIpvvnJX6d/4hzxfJ7p0VFKyVbrKp7LcffXv0nv1HTQwkspTlQr98TzecwQF64oRbRYZNvxE1zzwovY5TLHr72euaFd2BUV9Lfsj+NEq6ejSIubSyErR7ay2Bi6JsKJbJn0jMm53d14lkHPZAHL9VssBwVB14gl2CWX4bcyTe5izxTO7unBbzMPprn4JBZK9DfUCE7NlSh0RZjZ1rXiTYsyjboAdk8X2naxgeYbt5pHo5CKtDRwPvDhWT78q/+TM3/pY0eErpSJ78GZ0+W2PSM9F3I5n3R35+eNiNDTa9PTq7uBrCdaKDWLiDAzGpb6usg9X/kavVPTTS7aOx7/Hpn+fsb37WXvG4dbmkaL53H18y/QPzlVF6iBsxPMD/TzyC//Ykd9MjtCKQbO5pqsRkMRdCGZK5LpT1BIRRg5uUAkdO6qVYgHx7MYfnNqiukpRk/Mc+bKXl3sYA0Qz6d/It+c6qEgkatQyDsUV3C91zBcn1g+PAJ8KfMDCQQoJe2WBuXfKH6Oh97vMe4olB/kRxqGg2kIlWWCbIK0D590d0fD1Wwg9C2xpmN6pqdJz861dBgxXJdrDz3HW/uvZG5wAKdB+BzLQhlGk0gCWJ5H9+wce944fMHjMh2P/jNZdr45i+G1XqgMBclMNWpRhMmdaUoJCyXVTvamMLU91dJz0HQ8LKfV+hTA9FVHaSmaC8PwfJLV3NeW1xQkM9XXlMKqeNglNzwCRilGqu7+lfAsg2xfjEx/vEkkn3zgKT790Of53t8oKhVVb7Cs/MBaXE4kITB8bVvfWG1GtEWp6ZhYvhCaLmIAyWwOZRg8+vFfYP/Lr7Dvx6/jWjbFZII9h98MvYu3HYedR49x/Prr6J2aIlIqMzMyghuxES+Iml2pgo54PqMnFzC81oIEjTSmkPiWweSubgzXx/BVNdinzdbLBABFC07buatlaezKq2lGKaIFl3i2TDzvBO2rJLzpd62QvlXxGBzLYDl+tXi5MD3a1RTkFSs4mCE3PbX9NLpcZ0aSTd9N0Arrs/V5yGzG66yQ+lKEepNlzeZCf2uajpkZGcbwW0PoXdNkbN9eIEg7OXzrLRy+NQgaev+X/y503hLAJyiH95H/8ZckskEQkWeYPHPPffhWvLpvg9nRrrad4rsWyoi/gkgK5HpaU0h8y2C57EnPNvEsI4jADRv7KucoDdenbyJHIhe4potdNrPDXaEBSZsaXxEpB+UNnajZ+Q2BUgyMB30ea8JY6+sYurpALh1l+K0FTLd6Dqjgv8EzWc7u7annTFqV9t+0ZwbftRMxyfTFcapWZC1Q5+mH2m7aMZYNozsiWNqi3JRoodR0jBON8uKdd3Lg4EFsJ0jvcE2TUiJINwkjn07ji4Q2hPZNk5GxMRLZXP315+/6EBCpXxxt12dwLMPZPT0trlGAaNENjWStt+0SyKej5NOdzWMtZXJHitGTC6G5o6uyJpVi5NRCkys3nnMYKS1wZl/PlpnrTGTK9E/kA6tZVbtz9MbI9sVC2001Es85LWXnllKz+iDIh6yl9rS4xxUk50ssDAV9VNt1n/EFFgaT5HpimI7Dfx16hVf+05OIwMTfWHT3mC1FyFNpk4X5VqvSssDzGjy/AqYB23dHiMWMlv1oNg9bUij3q0mefOAp7v7qu9Z7KFuO1+64nfmhQa579hCxQpG3rryC12+7tW2/ytffdit7Dr/Z1J6rJmIv3/EObnzm2bpI5ru6yXX3o8zmi5ooSM8VmR3patm/EzXxc7RcXJVApjdGvifWvhJLBzgxi7N7uhk8vYBZNaZ9A6Z3pPHszvcbzzktlmlwkQ+aAC+XzH6pMTwfu+zhWgbekmMlftA02zON0BsVCCKDeycLRItOPdq4hvIV3TNF0rNFJnekKYd4BkzXp2u2WC8xuByeKWQG4hSTEdyISbJNNScBrIZqS+W4RSVqESkv3lgpwDeFr36uG+f3/5gH/7vH82VVF7rJsw6FXGsnj8Fhm2LBx6kF8wgYJuzaG6VUUszNuPVUDsOEzLyHm4SulBbLzcqWFMrCgnDwky/zaYKebzd/yOX3PvJrALz0YM9ym2o64My+vZypulpXYnZkmKfu/SDvfPQxDN/H8H0yPT088cBH6J6bb+r6UUp0ISokvQSwyuFVU3I9MdKzxaDISnWZApyIycJg4qLMAyYz5SDPrrqr83GU2hUv1IUo1cLu64JS9EwVSM+V8EUQpSjHbaa2p1CmkJwv0XcuHxxDpXAjJpM7Uk03CFbFY+TUQotA1qgXt1cweCbL2P7epu/EKnuMnloIuroQXvWpPlyg2BWpRydHCw6p2VLocfVlsTh5MABhclea7qkCXZkyouC62z3e/rff4rnbSmQXDCqV5sR+pSCX9SiXfKKxxW/dNIU9V0TJZ31KJZ9IROhKmxiGYEcCi7PWGaS2n4U5j2hU2Lk3irFFvAeXE1tSKJfy4iMWH3vkSwD80Ydc4j9/Ky/svZJ/91+WT4XQXBxOXXM1b+2/kp6ZWcqxKIV0GgAnFsPwFkWia2E2NFjIFygnwucoPctgYlc3/WdzRKqCU+iymR1dOb+uE2J5h9TS2rTVGrFLL/rL4URMVLX6SyNK2rsFLzXJhTKpuUBozKpCRIsO/WezZPrj9J2rpmRUX7PLHkOnM5zd21P/3N0zxbYiuRRBES26Td9l37l80xzzciLpG8LCQFUk8w5DY5lQC9SXoJ5rfknuozKEP/i/stxy4mjQOPnI4mv5vEfIPVpdLBuFEoL8xq60SVe69btTSjE+VmkR3XJZMT/r0jegcyI3G5eFUDby4iMWPPIy8DKfri6LPflRLZqXGGWazA01N9SuxGK8eNedHHj6IJbjEi0XGTxznMnt+1BmcGoqFue52uHELCb29iC+CqaNLuIde9dCuMVi+Ir0dAHfDPpkVmIWhXS0bV/MYpeNZxpIQ19QRSD0hVSESMklUU2DKKSjVOKtP81owSE9U8RyfYoJm2x/HM86/0Cg9Gx4cfpEPgg2Wvq5BbCcwE1bC3iJFN2ORLIdsUKbzjYsWpe+KRS7bOb7E3Vrtncy33ZueqE/HrTRavguGmuxHmzdDNuWmuHcwvSkS6WiGNlmd+Q6rZQVITFvKBW4YbVQbj4uO6EMo3T31+qieecrv83z0ye0cK4Rr73jdmZGhrn2uReIFotke2zmhpIkM05Qqixph3alrxEtFrEqDvl0qvPmzathmYjanplgfkwAX8r0TBc5uztNrOgult9LRQLrS4SJ3d30TubrrcQKXRFmh5N0TwdzePWqM/Mlsr0x5quBKADJhRJ9Z/P1lmN22aNrvsTZfT2rmittxGhXLF4FOaShn1uCIvO1khJOxAjcyh28nxIJys414BuCGVIkXxGUNCykIqGF6u1Ke3d1piqSf/I7E0Dw+2aFyNXuHouZqfDWXADZBY9YTOjtbxa5WuP7RgFdVku113VTooVyCU/f+FkAPk0wtwlwr/Fb6ziirc/E7t1M7N7dtGxhsM3KVaKFAj/xrYcYHjsTXIBjMZ7+0AcY37un7TaRYpFrnn+R7SdOkE+l+PHbb2N62+iy71NIR4nnnRbrZen1zlAgrs/24/PB6wqUAT1TBhO7u4NUFMtgZluKmYbtrLJHerbYUnUmNVci3x0NyvopFbQCW/L+hoLR4/MYBOX35geTFFMrRPf6QfH3ZLYS5ByGfJZgDApfWoOkUEFR+xqZgQTxfPtE/sbo46ntqRYVyfVEW1zbtdqq+ZCUnhqeZWA4rULvG8JN98/z8X/15Xonj+Xw/aCVlWUvdvJoZw3Oz3p1oSyXfM6NVygWg+3TPSZDI3Z1nlKwbGlphSUCPX36krsZERXma9jkXBPvUV+48uJHvOqgoA2CUvz0F/+GnunpphxNx7L49id+lUx/X8sm0UKBn/nLvw4sUM+rtxp7+gPv48T11y37XoNnskHpsw7m4pYKjyKoFTq9PRW6fnqmSM9UIbT27Pxggkx/HKvsse3E/Irv7QvMjHa1j6CtpqjYZa8p8jNsv75QzyGtresLzA/EWzqsxPIVhk5nw/djwNxggkI6Gp4e4isGx4PjWyssUI7bTO1o7yE4cN885Vd9it9V0GAEWr7LrXOvccv8G+Gfv4FS0WdivEK5FHypqbTJ8KiN5ylOHi2HumAtC664Oo7jKE4eLdGYHiwC8YTBzj3BsS+XfE6fDPZT21dXymR0R2fuW83F565XH3pOKXXb+Wyrb29WQWNQ0MfQbtq1wnQcLNelHIuBCH2Tk6Tn5loKGZiex7XPP8+P3vdTLfu4/plniRWLmNXgIYOg9N4djz/BqWuurrcAa0GC8nbp2SI9U8UVxxpW7i6Ra1/qTgmhlWfqlhjBHF0nGAp6pgpthTKZqTSJZNh4F8clnN3TTWquRCJXwTODsm5NkaRVSskIkztSDJ7JtliGM8NJCt3L9As1hKkd6aD8XNnDiZj1NJQD983zuTubLf6nb/xs3Y36avoKDvXdiCMWJj4H5t7g5g5E0nEUb50sLwbvqKDajlPx2bU3immCG+KFTaaCcc3POiz1FisFxYJPuewTjRpEYwb7roqRz/m4riKeMIjFtlhhicsILZQXQKObFgLhfM/vrnwx1XSGVa7wzke/y+4jR0AFHU6e/uD7sSsVVMhduaEUqbn50H3tPHa8LpJNKOienmFueKj9QETI9MVJz5RC59MadtU2MKUdhVSEnqlCyHtCoVokoe1cYghWiDuyRrtk/qXjrrk+lWmQGUiQGVi5R2epK8LkjjS9U/kgJ9M2mR9MrOgKrgXZhPIQPN04ziXzgTdkjnFd5jgVwybiOxjLHulF5med1ghXBeWSolxWjGyPcOatSlOlQdOk3keyVFKhX6pIEMgTrd6nGIaQComK1Ww+tFBeRJ6+8bM6KOgi8t6vf4PhsbF6i6vUwgLv/erXeeKBn20qYFDDNU0mdu0K3VcpkYCZ2ZblhudRji9j8dQQYWpHiqGxTN3cq0evyuJfww93vbbDs01mR5L0TeSbls8OB4E8oyfmsaqBK8vlGNb3t0wUrGcZ7V2tLL7gRAORWy3lpM1EcnFKohZMU+OK//PveOKvFZl5D6UgkTQY/poN0eUtLc9TTJ51yFRrrMbiwsi2SD1KNRr1V+XOLJfaCKqAU1akuk32XBFlfjaIdk0kDbp7LcyqZR+LGRTyfotYBmPRbtWtiBbKS0SjtfnOL9wEoCsFrYLU7CzDp8daSt+Zrsv+V14Nvdibnsfxa68J3d+Pb3sb/RMT9dJ7AJ4hzIwM1/M6V6KcsBm7so94toLhK0oJC8v1sSo+TtTEsQ1G3soEFXiqwTyeaTDXEL0aRr47RjEZIV510Ra7IviWweiJeeyl7cCoRtlW/y61BOcH2vfsrAXOhOFaQrY/gRM1V2yI3MiTDzwVuvzgJ19uCqZRSvHwCY9yabHyTSHvc+pEmX1XxjCt8PdTSjF2qtxkxZWKipPHglQaMYIskNEdEZJdnVlv8UQgdC3zkAqisWAckajB0Gj4DU5vn8X8rNvkfhUJhD+yguhrNidaKNeAg58MKgTVKgUB/N2f/ZIOCGqD+D53PvIoEhJRYQDDp0/jG0bLHKVrWWw7dYqjN93Yst3p/Vfy2tvfzk0Hf9iwX+HwgQOrGpsyhEJDjVc3CjTo4Pi+HuK5CnbFx4mYFLvsZTqTBBGo6dkSpqcoxS3mh4JUGLvsYrVJu3AsCfIEFXTPFjG8oMvK/EBi2UhRJ2qFWpQC2K4i1xNtO9ZaB42lHOywYHi5pJpEsobyYX7epb9NbmFtu3ZeVeWDB5x5q8LeK6PYkZWFqrvXYnbGRTV44kUg0dWZ0Fm2sGtfNChxl/cxDOjuMRkY1vmRWxUtlOvEx/7ll/hY9bF20zZz8z//gKEz423n+3zDwApxvZqeR6wQMt9XpWdmBl8Eq3q1Nn2fO7/7GNm+3hXTRDpGhGIqSicz1T1ThabUiHjeIXZqgbN7eoK+mkKLQAjgWWYglEC2L7ZoZnZgBSpDaIlEASxL8dnfPsfbhvaGCuKFdtAol8PnTpWCcrH93GKl4rctBLB0P2dOV0h2GXSlTGLx9nVVLUvYvS/K1IRDPu9jSCCeA4OdXw6j0cUIV83WRwvlBqDRTXu5VwkS3+fa519Ytp7q2L597H/1NWzHaVruWRbnduwI3SaWz7Pz6LEWK9RwXW744TN8/6P3t4xj15tHuOrFlxDl88att/LWVfsvWg9J8fyW/EEB8IOUkbmhZKgV5UtQ5WdxIwnSG+bmufmpHzB8eoxSIsErd9zOqWuuBoKAmT/6xl8B8I3uA3xv/hoctfjTN32PK2dPUr7nUFPwzMWknaUmAtF4+2MajRorimSNwPr0mJvxSKVNRra3pmKUyz5z0y7lsk8sZrD3is6sUM3ljRbKDUZjlaDYkx/li2/GLisXrem6mGGx+VUc2+aFn3gX3XPzDI2NYVfXdWybiV07mdq+LXS7ZCaLZ5otka8GkJ6ba15ZKe7+6tfZfvJUfY505PQZpkZHeORXfumiiKVd8YLI3SUqIAStw5QpzA8mghxLVav+EwTk1Mr5HbgviPC1J7Jc/evfwCxUEB+SuRx3f+vb9P/wO/QP2vAQvFj9qe/kdbYN9zCWGMFUPp4YDJdnuGv6xQv+TMsRiwnRmLS4X8WA7m6TYiEI8InFjaai4dGYQTxhUCyEzCm2QVXTPdI9ZtO8ZbHgcfrkYjRrqeiRWfDYtTfaUstVo2lEC+UGpnT31/gYizmbwJZPP3Ftm2JXF8lstuU1zzB4/Bd+Djca5YkHPsKVr7zKla+8BgJHb7yBozfe0FbEMn29GCENpH2RFnEdPfVWk0hCIFSDZye49tBzvP721pxl8X0SuRzlWAw3snLvS9c2Q+dgFUFZOIBsXxwnavGBkQqZeY9bbo9zz4e6SLz+RDDvXXWHToxXWMg13wAoBTNTLr39FiIwO+1W2z/Btcee4JbdfRTSvXQ7WfqczIrjbRm/o5iecshnfQwT+vot0iG9G2uICDt3R5mccMgsLEa9dveanDzWnOA/sj3SlFaxfVeE6UmHhXmv3taqTS/wps+fmfeahPLcuNMitr4PkxOOdqNqlkUL5SZhac7mzR9yt2ZpPRGeee97ePdDj9TnIX2CJs+PfuznmK6KmjJNjtx8gCM3dxaM40SjjO/ayc7jJ5qKkisRXrnj9qZ1dx4+EipiAlx36PkWobzilVd5+5Pfx3Q9RClOXHM1Bz/wPnwr/OcVKZVIZjI4URO73FwmzvZdfvX17zL40lzrht+Bl/6wdXExH64atby+zILL/Ky3aEmVFOU3Z9i1L3deSfCeqzh5rETdOHfh3FmHUslnuE2kKIBhCiPbI4xsD577vuLY4VKL6I2frrBv/6JL1DCEoZEIQ9UZiVoLq5UszEbNVn6QIxlGsdB5nqrm8kQL5SblxUcsPs3ngUA03/j3vwCwJeY337r6Kp6IxTjwg4Ok5+aYHR7ihXfdxezI8HnvM5YvsO3kqZbGyUqEaLFErsG77S9TZNxym+dFt504yR2PPdEUXLTn8JuIUjz10/c25RIq12fs955l7q+O1gNUctu28fzN78UXIeEUuevcIQadEJFcBjsiVCoh1qkCw6BJJBtfm5l02L5r9ZbU3KzbIm61nov9gwqrTarHUnJZr22JgImzFXbuDo/gjSeMUDduI7X6q4sLaBsUFNLZTaNpQgvlFiBoHfY1YDFvc7P325zYvYuJ3eHFA86HHcePo0yzxWdneB573niDmdHFY/X6Lbdw3aHnW/ahgNNXXNG07MaDP2qJwLVcl/1H3+ADD56g9NCiaEydc5ibcZvrf46P8+7xv8G1I1hOBQeYHrDqVWA6oW/AopBvtrBEINlVDYQJiZ6FZRLvVyA0B7H6nuWSj9VhPqPvEdoDEqCQU7huuOiGuXFr71+jt88kkTSbtunuMQP37ZLjpAuVa1ZiXc4QEfl54D8B1wK3K6UOtVnvg8DnABP4C6XUZ9ZskJuYIG/zZV1ar4G2kiBL0/Yh39vDa7fdyvVVsazpjBON8O/uOsxPP3S8vu6xcyXCQo+Uq/BcVa/molTQtDfMsgOwKpX6OGenXRJJo+lCvxyJpMnwNpvJicXSbF2pIOrT91VbMaol16+WSEQohmThKEXH1iQEc5TtEAkszp7e8EvUUjeu5yqy2WAOM9kmH3JwxMZ1FfncYspJqtukfxVpIZrLk/U6Q14FPgr8WbsVRMQE/hvwPmAMeFZEHlRK/Xhthrh1WFpaD7Z+UNBSxq7Yxx2PPd6y3DdNfu+/H2Dg1uaKNupZmyc+FmHqnIPnQVfKoK/f4M2nmsUrHjfIOq01ZIUgMb3pvTqcCqu5MZcTykLeY37Ww/MUXamgxFq628R1FIYpKAVnxyrksu3nL/sHzy9BvrffarLkakRjsqro0UjUIBqFcjn89dXIuGlJW1GtYRjC9l1RHMfHqSgiEaPlO9JowlgXoVRKvQ60jZCrcjtwVCl1vLru3wL3A1ooL4DLtd9mOZHgvX/5M/zg178BBC4/MaC/F978xJd4M2SbRNJk977lrbqBIYtczmuy2kRgYNBqSnMQESJRodImoGQp/jKRKrPTDtOTi9ZpseCzMOexa18QAKOU4sSRMo4Tvo9IVBgetYnFlxc1x/GZn/VwHEUiIaR7gs8UjRls2xlhYnyxd2M8abBte3Mgj1IK3w/mANv91oe3Rzh9Ijwwp9at42Jj2wa2LqKjWQUb2eewHTjd8HwMeMc6jWVL8uIjwddfCwqCoLQebJ5+m7VcwkY+cVUp6Gq/hHMPwZ59MbIZD6UUXSnzgmtzRqIGu/dFmT7nUCz6WJbQP2iHdo0YHrU7jtZMp8N/mp6nmkQSAgu0UlEszLv09tnksz6uFxK1a8DwiE33CpYXtEaW5jIwO+2xe18U0xK6UiZXXBXDcRSmIS21WjPzLpPnHDw3eN++fov+QatFMONxk74Bi9npZgf28DZ7VW5cjeZScsmEUkQeB8KiSf6jUuqbnewiZNkyU03yKeBTAMN2+8LQmuWptTz6GEFQkLz9fevupv3+Z+KoZx9rWd6YS9hIeNnvAMsWevsv7mkfiQiJpEGppKiUFXMzLrYtLRZbImmya2+UmSmHclkRixlEosLstNsUkJJIGnSlwwW8WAgv6aYU5DI+vX1B2beweUnlB6+thFKKs2ecFjF2XMXMtMPQSKQ6ViESaf2Z5rIeEw05i8qn/hkHQ+qhDgzZpHtMctngs6VSpnaJajYUl0wolVKt3XNXxxiws+H5DmB8mff7c+DPAa6J95xfOJ+miYOffJl3fgGefGDtOp887P9p3dKtcaF1Ri8105NuPZoVAjF760SZ3ftaK77E4sF84vysi+MoYnFh194ImQUP3w+CcJJd7euUmmb7u8WaVReJGojRGlEqRlASbiVcJwhEakFBNuPX8xkhyIX0PTCtRffq9GRrYr9SMDfjMjBoIUbrZ4tEgjlgjWYjspFdr88C+0VkL3AG+DjwS+s7pK1P7MmP1h//u/8yAl9d2/e/1/gtDvzZMs18Nxi+p5pEskZQGcdh287mPMXGFBEI0ikikaAbhREiIEuJxQ1MU3CXFDYXCVIiIIj6tO3W+VDThK4OGgmHCVmN2ktKKabPOczNevX3Hxi06B2w286NAngeLNMyU6PZkKxXesjPAv8PMAg8JCIvKqU+ICLbCNJA7lVKuSLym8CjBOkhX1BKvbYe493qNLX8+i/rOxYI5kdf+vC/4U9+ZyJ0rnEj4TiqbSJ7aUlXDNdpFdXa/GJ2weto7jDIIYwwdqqC66kgdUXB4IhFPGHW19m1N8gzzC4EQtaVMhkatTsSY8sK6rIuHX+Qcxi8x8yUy1xDIQOlYGrSxTCFaNQIrXYjElieGs1mY72iXr8OfD1k+Thwb8Pzh4GH13BoW5ranCMsSQ95cJ0GtAWwbGkbnBNZ0u2+WFxmfjHbmVAG+zXYuz9KqajwfVW3MhsxTWF0e4TR7R1/lCa27Yxy+kR5MShIBWLb02eh1DJW9LTL6PYIp082128VCSKEV4h012g2JPr+bovT1IHkq8BXL6/8yUuNaQrpHpNMSMWXpXmKncwvdoqIEE9cOtGxbWHv/ijFgl+dSzXq85u+p9rmhLqOIp4w2LEnwtREELQURAJbdPfoy41mc6LP3C1CLScS4Pc+8msbypW61RketTEN6q5I2w7yFOOJ5sm4eMLANMBdGmQj0LsBy6iJSGjRAzHAsiCsG1qt2k8isXIOqkazWdh4v05Nx9z5ym/z/PSJ1pquW8SVesuJoxxc70F0gIgwOBJhYFhVC5Ev02pqTzSYX3Src5sEQruZ+iEGn9dmYkkKiUh4+odGs9nRQrnJiD350UVh/N0i4amqm5/vfybO0ze+vN7DWBUismJP59r8Yrms8D3V0qh4s5DutjANYXrKoVJRRKMGg8OLAUUazVZCC+UG5Z1fuAkIyV/UrtRNj4gQO8+C5BuJZMq8ZGXmNJqNhBbKDUSTK3WN8xc3Egfum+fpGz+/8ooajUazBmihXEea8hdhS7tSV8MffeOveFGfmhqNZoOgr0ZrRC2H8d8+fXZRHLdI0I1Go9FsZbRQXiJq+YvAkhzGzdGVY7148oGnOPhJfVpqNJqNg74iXQQ0XmRYAAAHcElEQVRqOYxv/PtfWIxI1UE3q+bAffNBRxCNRqPZQGihPA9qQTcAX3wzxu/r5H6NRqPZsmih7JA7X/ntxfqoOujmonPgvs3TMUSj0VxeaKFcQi1/EeCFvVcuSe7XXCo+d+coT6/3IDQajSYELZQE4vi/OjcsBt1oNBqNRlPlshPKWuBN4o//w6IrVYvjuhKUq/vseg9Do9FoQtnyQnnzh1wSf/wfmvMXQbtSNxDq2cfWewgajUbTli0plF174vz+h//N4oLf1fmLGo1Gozk/Nk9vn1VweL5rvYegWQXFf3h+vYeg0Wg0bdmSFqVmc1BLCdF1XTUazUZmS1qUGo1Go9FcLLRQataNT1xVWu8haDQazYpoodSsC3/yOxOU7v7aeg9Do9FoVkQLpUaj0Wg0y6CFUrMu3HLi6HoPQaPRaDpCC6VmzQl6Tup2WhqNZnOghVKj0Wg0mmXQQqnRaDQazTJoodRoNBqNZhnWRShF5OdF5DUR8UXktmXWOykir4jIiyJyaC3HqNFoNBoNrF8Ju1eBjwJ/1sG6dyulpi/xeDQajUajCWVdhFIp9TqAiKzH22s0Go1G0zGilFq/Nxf5PvA7SqlQt6qInADmAAX8mVLqz5fZ16eAT1Wf3kBgtV7ODACXuyWuj4E+BqCPAehjAHC1Uip1PhteMotSRB4HRkJe+o9KqW92uJu7lFLjIjIEPCYibyil/ilsxaqI/nn1vQ8ppdrOfV4O6GOgjwHoYwD6GIA+BhAcg/Pd9pIJpVLqpy7CPsarfydF5OvA7UCoUGo0Go1GcynYsOkhIpIUkVTtMfB+tDtVo9FoNGvMeqWH/KyIjAHvBB4SkUery7eJyMPV1YaBp0TkJeAZ4CGl1Hc6fIu2c5mXEfoY6GMA+hiAPgagjwFcwDFY12AejUaj0Wg2OhvW9arRaDQazUZAC6VGo9FoNMuw6YVSl8MLWMVx+KCIHBaRoyLyu2s5xkuNiPSJyGMicqT6t7fNel71PHhRRB5c63FeClb6XkUkKiJ/V339RyKyZ+1HeWnp4Bj8uohMNXz3/2I9xnmpEJEviMikiIQGPUrAn1aPz8sicutaj/FS08ExeI+ILDScA3/QyX43vVCyWA6vk7SRu5VSN2/RfKIVj4OImMB/Az4EXAf8oohctzbDWxN+F3hCKbUfeKL6PIxi9Ty4WSl139oN79LQ4ff6G8CcUupK4L8C/3ltR3lpWcW5/XcN3/1frOkgLz1/CXxwmdc/BOyv/vsU8N/XYExrzV+y/DEA+OeGc+APO9npphdKpdTrSqnD6z2O9abD43A7cFQpdVwpVQH+Frj/0o9uzbgf+GL18ReBj6zjWNaSTr7XxmPzFeAe2Vo1JLf6ub0i1WIss8uscj/wVyrgh0CPiIyuzejWhg6OwXmx6YVyFSjguyLyXLXc3eXIduB0w/Ox6rKtwrBS6ixA9e9Qm/ViInJIRH4oIltBTDv5XuvrKKVcYAHoX5PRrQ2dntsPVN2OXxGRnWsztA3DVv/9d8o7ReQlEXlERK7vZIP16h6yKta6HN5G5SIchzALYlPlBy13DFaxm13Vc2Ef8D0ReUUpdezijHBd6OR73fTf/Qp08vm+BXxZKVUWkX9FYGG/95KPbOOw1c+BTnge2K2UyonIvcA3CFzRy7IphFKXwwu4CMdhDGi8i94BjF/gPteU5Y6BiJwTkVGl1NmqS2myzT5q58LxamH+W4DNLJSdfK+1dcZExAK6uQQuqnVkxWOglJppePr/scXmaTtg0//+LxSlVKbh8cMi8nkRGVipleNl4XrV5fDqPAvsF5G9IhIBPg5siajPKg8Cn6g+/gTQYmWLSK+IRKuPB4C7gB+v2QgvDZ18r43H5ueA76mtVW1kxWOwZD7uPuD1NRzfRuBB4Neq0a93AAu1qYrLBREZqc3Ni8jtBBo4s/xWgFJqU/8DfpbgTqkMnAMerS7fBjxcfbwPeKn67zUCV+W6j32tj0P1+b3AmwQW1JY6DgRzbk8AR6p/+6rLbwP+ovr4TuCV6rnwCvAb6z3ui/TZW75X4A+B+6qPY8A/AEcJSkLuW+8xr8Mx+KPq7/8l4EngmvUe80X+/F8GzgLO/9/eHaPUEUVhAP4PNgaeTVZhkTJpLN1HbGxeIdmBhZIuO7BWV2ARC1sRUqdM5wIEmxTCTTFTpLqIPBlm3ve1A8NhuMwPh8s547/gOMk6yXp8XhluBv8Zz/7nqWue4Buc/HcGHpIcvOa9RtgBQMdWtF4B4K0EJQB0CEoA6BCUANAhKAGgQ1DCglXVz6p6qqqbqWuBuRKUsGw/knydugiYM0EJC1BVX8Zh37vjJKrfVfWptXaX5Hnq+mDOZjHrFehrrf0al1B/T/IhyWVrbRvHNMLGCUpYjvMMM0//Jvk2cS2wGFqvsBwfk6yS7GWY7QpsgKCE5bhIcprkKtu3QgrejdYrLEBVHSV5aa1dV9VOkvuqOkxylmQ/yaqqHjNsS7mdslaYG9tDAKBD6xUAOgQlAHQISgDoEJQA0CEoAaBDUAJAh6AEgI5/3OvuEWh45sMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2534393bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.reshape(300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n",
    "- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n",
    "- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n",
    "\n",
    "<font color='blue'>\n",
    "**In summary**:\n",
    "- Initializing weights to very large random values does not work well. \n",
    "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - He initialization\n",
    "\n",
    "Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n",
    "\n",
    "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n",
    "\n",
    "**Hint**: This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_he\n",
    "\n",
    "def initialize_parameters_he(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims) - 1 # integer representing the number of layers\n",
    "     \n",
    "    for l in range(1, L + 1):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * np.sqrt(2.0/layers_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.78862847  0.43650985]\n",
      " [ 0.09649747 -1.8634927 ]\n",
      " [-0.2773882  -0.35475898]\n",
      " [-0.08274148 -0.62700068]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_he([2, 4, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 1.78862847  0.43650985]\n",
    " [ 0.09649747 -1.8634927 ]\n",
    " [-0.2773882  -0.35475898]\n",
    " [-0.08274148 -0.62700068]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using He initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.8830537463419761\n",
      "Cost after iteration 1000: 0.6879825919728063\n",
      "Cost after iteration 2000: 0.6751286264523371\n",
      "Cost after iteration 3000: 0.6526117768893807\n",
      "Cost after iteration 4000: 0.6082958970572937\n",
      "Cost after iteration 5000: 0.5304944491717495\n",
      "Cost after iteration 6000: 0.4138645817071793\n",
      "Cost after iteration 7000: 0.3117803464844441\n",
      "Cost after iteration 8000: 0.23696215330322556\n",
      "Cost after iteration 9000: 0.18597287209206828\n",
      "Cost after iteration 10000: 0.15015556280371808\n",
      "Cost after iteration 11000: 0.12325079292273548\n",
      "Cost after iteration 12000: 0.09917746546525937\n",
      "Cost after iteration 13000: 0.08457055954024274\n",
      "Cost after iteration 14000: 0.07357895962677366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2534393bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9933333333333333\n",
      "On the test set:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25340459160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.reshape(300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 5 - Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **Model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Problem/Comment**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN with zeros initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        50%\n",
    "        </td>\n",
    "        <td>\n",
    "        fails to break symmetry\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with large random initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        83%\n",
    "        </td>\n",
    "        <td>\n",
    "        too large weights \n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with He initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        99%\n",
    "        </td>\n",
    "        <td>\n",
    "        recommended method\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember from this notebook**:\n",
    "- Different initializations lead to different results\n",
    "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n",
    "- Don't intialize to values that are too large\n",
    "- He initialization works well for networks with ReLU activations. "
   ]
  }
 ],
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